Advances in Geosciences: Volume 13: Solid Earth (Se)

A d v a n c e s

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Geosciences Volume 13: Solid Earth (SE)

ADVANCES IN GEOSCIENCES Editor-in-Chief: Wing-Huen Ip (National Central University, Taiwan) A 5-Volume Set Volume 1: Volume 2: Volume 3: Volume 4: Volume 5:

Solid Earth (SE) ISBN-10 981-256-985-5 Solar Terrestrial (ST) ISBN-10 981-256-984-7 Planetary Science (PS) ISBN-10 981-256-983-9 Hydrological Science (HS) ISBN-10 981-256-982-0 Oceans and Atmospheres (OA) ISBN-10 981-256-981-2

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A d v a n c e s

i n

Geosciences Volume 13: Solid Earth (SE)

Editor-in-Chief

Wing-Huen Ip

National Central University, Taiwan

Volume Editor-in-Chief

Kenji Satake

University of Tokyo, Japan

World Scientific NEW JERSEY

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ADVANCES IN GEOSCIENCES A 6-Volume Set Volume 13: Solid Earth (SE) Copyright © 2009 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the Publisher.

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(Set) (Set) (Vol. 13) (Vol. 13)

Typeset by Stallion Press Email: [email protected] Printed in Singapore.

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EDITORS Editor-in-Chief:

Wing-Huen Ip

Volume 10: Atmospheric Science (AS) Editor-in-Chief: Jai Ho Oh Editor: Gyan Prakash Singh Volume 11: Hydrological Science (HS) Editor-in-Chief: Namsik Park Editors: Joong Hoon Kim Eiichi Nakakita C. G. Cui Taha Ouarda Volume 12: Ocean Science (OS) Editor-in-Chief: Jianping Gan Editors: Minhan Dan Vadlamani Murty Volume 13: Solid Earth (SE) Editor-in-Chief: Kenji Satake Volume 14: Solar Terrestrial (ST) Editor-in-Chief: Marc Duldig Editors: P. K. Manoharan Andrew W. Yau Q.-G. Zong Volume 15: Planetary Science (PS) Editor-in-Chief: Anil Bhardwaj Editors: Yasumasa Kasaba Paul Hartogh C. Y. Robert Wu Kinoshita Daisuke Takashi Ito v

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LIST OF REVIEWERS

The Editor of Volume 13 (Solid Earth) would like to thank the following referees who have helped review the papers published in this volume: Agustan Giulio Barbieri Jungho Cho Phil Cummins Takashi Furumura Richard Gross Martin Flower Nuraini Rahma Hanifa Hasanuddin Z. Abidin Takahiro Hatano Yukio Hayakawa Kosuke Heki Giulio Iovine Shuanggen Jin Yasuyuki Kano Teruyuki Kato Somboon Khositanont Hamzah Latief Kuo-fong Ma Norio Matsumoto

Ritsuko S. Matsu’ura Irwan Meilano Ki-Bok Min James Mori Hiroshi Munekane Ken T. Murata Danny H. Natawidjaja Jong Uk (James) Park Bill Petrachenko Marco Piras CP Rajendran Lucas Donny Setijadji D. Srinagesh Yuichiro Tanioka Hiroyuki Tsutsumi Yasushi Watanabe Pornsawat Wathanakul Moriaki Yasuhara Jiancang Zhuang Michael Zolensky

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CONTENTS

Editors

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List of Reviewers

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Mineralization Characteristics and Ore Fluid of Huai Kham on Gold Deposit, Northern Thailand Somboon Khositanont, Khin Zaw and Prayote Ounchanum Formation of Hollow Concretions in Northeastern Thailand Prinya Putthapiban and Sutatcha Hongsresawat Investigations on Local Quartz Sand for Application in Glass Industry Pisutti Dararutana, Prukswan Chetanachan, Pornsawat Wathanakul and Narin Sirikulrat Geochemical and Sr–Nd–Pb Isotopic Study of Late Neogene Volcanic Rocks from the Arita–Imari Area (SW Japan): Evidence for Coexisting OIB-Like and Subduction-Related Mantle Sources Nguyen Hoang, Jun’ichi Itoh, Kozo Uto and Akikazu Matsumoto Landslide Hazard Zoning of the Muravera Hillside (Sardinia, Italy) Giulio Barbieri and Paolo Cambuli

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An Integrative Geoscience Data Model by Linking Field-Specific Data Models in Digital Geologic Map, Earth Resource, and Geo-Hazard Lucas Donny Setijadji and Koichiro Watanabe Crustal Deformation Monitoring by GNSS: Network Analysis and Case Studies Marco Piras, Marco Roggero and Maurizio Fantino

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The Global Geodetic Observing System H.-P. Plag, M. Rothacher, M. Pearlman, R. Neilan and C. Ma

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The International Laser Ranging Service Michael Pearlman, Carey Noll, Jan McGarry, Werner Gurtner and Erricos Pavlis

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Earth Rotation Parameters from Very Long Baseline Interferometry and Ringlaser Observables P. J. Mendes Cerveira, H. Spicakova, H. Schuh, T. Kluegel, U. Schreiber and A. Velikoseltsev Toward a New VLBI System for Geodesy and Astrometry J¨ org Wresnik, Johannes B¨ ohm, Andrea Pany and Harald Schuh

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Periodic Station Motion in Gothenburg Observed with GPS — Possibly Related to Hydrological Phenomena? R. Haas, N. Tangdamrongsub, H.-G. Scherneck and J. Johansson

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Evaluation of the Coseismic Pore Fluid Pressure on a Thrust Fault Jeen-Hwa Wang

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The Water Level Changes of the Aneuklaot Lake, Weh Island after the 2004 Sumatra–Andaman Earthquake Agustan, Djoko Nugroho, Lena Sumargana, Irwan Meilano, Mohd. Effendi Daud, Fumiaki Kimata and Yusuf S. Djadjadihardja Effect of Near-Source Trench Structure on Teleseismic Body Waveforms: An Application of a 2.5D FDM to the Java Trench Taro Okamoto and Hiroshi Takenaka Numerical Modeling of the 2006 Java Tsunami Earthquake Nuraini Rahma Hanifa, Irwan Meilano, Takeshi Sagiya, Fumiaki Kimata and Hasanuddin Z. Abidin Why Many Victims: Lessons from the July 2006 South Java Tsunami Earthquake Hasanuddin Z. Abidin and Teruyuki Kato Earthquake Potential in Myanmar Hla Hla Aung

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Looking into a Sandpile by Photo-Elasticity and Discrete Element Method Naoto Yoshioka and Hide Sakaguchi

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Statistical Properties and Time Trend in the Number of Holocene Volcanic Eruptions A. N. Zemtsov and A. A. Tron

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Advances in Geosciences Vol. 13: Solid Earth (2007) Ed. Kenji Satake c World Scientific Publishing Company


MINERALIZATION CHARACTERISTICS AND ORE FLUID OF HUAI KHAM ON GOLD DEPOSIT, NORTHERN THAILAND SOMBOON KHOSITANONT Geological Sciences, Chiang Mai University, Chiang Mai, 50002 Thailand [email protected] KHIN ZAW CODES ARC Centre of Excellence in Ore Deposits, University of Tasmania, Hobart, TAS, 7005 Australia PRAYOTE OUNCHANUM Geological Sciences, Chiang Mai University, Chiang Mai, 50002 Thailand

Huai Kham On gold deposit is located within the Lampang-Phrae volcanic belt of the Sukhothai Fold Belt in northern Thailand. The gold deposit is hosted by Triassic andesitic tuff and intercalated rhyolitic welded tuff. The host sequence is overlain by sedimentary sequences including Triassic calcareous sandstone of the Wang Chin Formation and Middle Triassic limestone of the Kang Pla Formation. Gold nuggets and electrum were found in quartz-rich veins with associated pyrite, chalcopyrite, galena and bournonite cutting across the andesitic tuff and underlying rhyolitic tuff. Sulfur isotope analyses indicate that the gold-ore forming fluids vary in sulfur isotopic values from −5.3 to −3.5 per mil. Fluid inclusion studies indicate that the ore-forming fluids were typically enriched in CO2 which is evidenced by the occurrence of carbonic (CO2 (L)– H2 O(L)–CO2 (V)) inclusions in quartz adjacent to the gold-bearing sulfide minerals. Three types of fluid inclusions including Type I (L-V) aqueous inclusions, Type II (L-L-V) aqueous–carbonic inclusions, and Type III (V) vapor rich inclusions, were classified in vein quartz. Results from a preliminary microthermometry study of fluid inclusions in ore-bearing vein quartz indicate that the melting temperature of CO2 solid varies from −56.8◦ C to −56.6◦ C and that the homogenization of the carbonic phase varies from 28◦ C to 31◦ C suggesting that the carbonic phase contains pure CO2 . This interpretation is supported by Laser Raman Spectroscopy analyses. The homogenization temperatures of the carbonic inclusions in quartz vary from 280◦ C to 300◦ C Salinities of ore fluids range from 1 to 7 wt.% NaCl equiv. 1

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S. Khositanont, Khin Zaw and P. Ounchanum On the basis of available data such as vein texture, alteration, and fluid inclusion data, the Huai Kham On gold deposit is comparable to orogenic gold deposits.

1. Introduction The Sukhothai Fold Belt in northern Thailand has formed as a result of Indochina–Shan Thai collision.1−4 The formation of N–S trending orogenic belt in conjunction with volcano-plutonism in relation to subduction and collision has produced a large variety of precious and base metal deposits, e.g. tungsten, antimony, copper, and gold. A number of gold deposits which have been discovered within the Sukhothai Fold Belt are also located in the volcano-plutonic aureole including Huai Kham On deposits in Phrae Province. However, the gold mineralization at Huai Kham On deposit has not been well studied. This study reports gold mineralization and fluid inclusion characteristics at Huai Kham On deposit in the Sukhothai Fold Belt.

2. Geology of Huai Kham On Deposit The Huai Kham On deposit is hosted within the NNE-SSW trending Triassic volcanic rocks, named as Lampang-Phrae volcanic belt, and the overlain by Triassic sedimentary sequences of the Kang Pla Formation and Wang Chin Formation of Lampang Group. The lower part of the volcanic sequence at the Huai Kham On deposit is composed mainly of andesite porphyry, andesitic tuff, and breccia. Pyrite and other sulfide minerals are rarely observed in the andesite porphyry. The overlying andesitic tuff contains breccia within a welded matrix. The andesite sequence is overlain by rhyolitic and welded rhyolitic tuff. The rhyolite, composed of angular translucent to transparent quartz and subrounded pink feldspar, is set in the dark reddish brown fine grained groundmass. Some rhyolites are composed of angular to subangular quartz phenocrysts with fragments of subhedral K-feldspar and plagioclase. Large pyrrhotite crystals with fine-grained pyrite replacement are observed elsewhere in this rock. The uppermost successions of volcanic rocks at the Huai Kham On deposit are overlain by middle to late Triassic sedimentary rocks including limestone of Kang Pla Formation (Lst) and clastic rocks (Tr) of Wang Chin Formation.5

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3. Vein Mineralogy and Alteration Quartz veins are found only in the Triassic andesitic tuff and intercalated rhyolite (Fig. 1). The veins are 50 cm to 3 m thick and delineate along

Fig. 1. Geological map showing major rock units and significant geological features at Huai Kham On deposit, Wang Chin District, Phrae Province.

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Fig. 2. Gold and galena association in quartz vein from Huai Kham On deposit. Au: native gold, Gn: galena, Gtz: quartz.

northwest strike. Quartz veins are enclosed within chlorite alteration zone. Most of vein quartz in the gold-bearing quartz veins show cataclastic texture suggesting that the gold-bearing quartz veins were formed in the orogenic environment. Pyrite is the most abundant sulfide mineral, whereas chalcopyrite, galena, and bournornite are much less abundant. Pyrite generally has a reaction rim with galena inclusions. Gold grains are specially associated with galena or included by quartz (Fig. 2). Results from Laser Ablation Inductively Couple Plasma Analyses indicate that pyrite also contains dissolved gold where chalcopyrite is present.

4. Sulfur Isotope Study Sulfur isotope study is applied to the sulfide mineral from five vein quartz samples of various depths at the Huai Kham On deposit. Pyrite grains were drilled from gold-bearing quartz veins. Sample preparation for sulfur isotope analyses was carried out using quantitative preparation of SO2

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Mineralization Characteristics and Ore Fluid of Huai Kham Table 1. deposit.

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Sulfur isotope analyses of pyrite from Huai Kham On

Sample ID

[email protected] [email protected] [email protected] [email protected] [email protected]

Mineral

Weight (mg)

d 34 SCDT (permil)

pyrite pyrite pyrite pyrite pyrite

14.2 31.0 12.7 14.0 12.3

−3.4 −4.6 −4.2 −5.5 −5.4

for 34 S/32 S analyses from sulfides by combustion with cuprous oxide technique.6 The sulfur isotope analyses were performed with VG SIRA Series 2 triple-collector mass spectrometer at University of Tasmania. Sulfur isotopes of pyrite from the Huai Kham On deposit show a narrow range from −3.4 to −5.5 per mil (Table 1). The S isotope ranges for magmatic source generally show small positive values or close to 0 per mil and the isotope value for sedimentary source generally shows much greater positive value.7

5. Fluid Inclusion Study Fluid inclusions are observed in quartz. Primary fluid inclusions are observed in crystal growth zones by polarizing microscope (Fig. 3) and by cathodoluminescence — SEM image.

5.1. Classification of fluid inclusions Fluid inclusions in quartz are classified into three types as follows: 1. Type I (L-V) aqueous fluid inclusions are composed mainly of aqueous liquid with a relatively small vapor bubbles (Fig. 4). They generally occur as secondary origin along fractures that cut across the crystal boundaries of quartz. 2. Type II (L-L-V) aqueous–carbonic fluid inclusions show aqueous and carbonic liquids and a gas bubble at the core of the carbonic liquid at a room temperature (Fig. 5). Carbonic gas and carbonic liquid phase separation is observed at temperature below 31.1◦ C (critical point of CO2 ). The presence of CO2 as a major component in the carbonic phase is also confirmed by Laser Raman Spectroscopic Analyses. These

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Fig. 3. Photograph of crystal growth zone (dashed lines) and fluid inclusions in quartz at Huai Kham On deposit.

Fig. 4. Type I fluid inclusions in quartz from Huai Kham On deposit; V = vapor bubble, L = aqueous liquid.

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Fig. 5. Type II (L-L-V) aqueous carbonic fluid inclusions in quartz from Huai Kham On deposit. Low-density CO2 phases are squeezed in the middle, surrounded by aqueous liquid at rim.

inclusions are aligned along crystal growth zones in quartz crystals, suggesting a primary origin. 3. Type III (V-L) aqueous–carbonic inclusions generally show large volumes of carbonic phase including liquid and gas (Fig. 6). The CO2 liquid may not be observed at room temperature due to insufficient CO2 in the fluids. Laser Raman Spectroscopy analysis shows that the carbonic phases are composed only of CO2 . These inclusions are of primary origin due to their appearance along crystal growth zones.

5.2. Microthermometric measurement and P–T–V–X relation Microthermometric measurement was carried out using a Linkamcomputerized fluid inclusion freezing/heating stage. The thermocouple was calibrated from −56.6◦C (melting point of pure CO2 ) to 374.1◦C (critical point of pure H2 O) using Fluid Inc synthetic fluid inclusions. The results can be reproduced within 0.1◦ C for the freezing experiment and 1.0◦ C for heating experiment. The inclusions were cooled down to −120◦C while all phases were frozen. Then they were gradually heated up to −56.6◦ C to observe the

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Fig. 6. Photograph of Type III vapor-rich inclusions in quartz from Huai Kham On deposit. Inclusions contain mainly CO2 gas bubble (dark part inside the inclusions).

melting point of CO2 for Type 2 (L-L-V) inclusions and Type III vaporrich inclusions. The heating experiment was carried on to 10◦ C in order to observe the final melting temperatures of the solid phases including ice and clathrate (mixed CO2 –H2 O solid). Finally, all inclusions were heated up to observe the CO2 homogenization temperature in Type II (L-L-V) and Type III inclusions and the final homogenization temperature. The melting temperature of carbonic ice in Type II (L-L-V) inclusions ranges from −59.2◦ C to −56.6◦C suggesting that CO2 is the major component in the carbonic phase for Type II (L-L-V) inclusions. The homogenization temperatures of CO2 phase range from 25◦ C to 31.1◦ C, which also support CO2 as a major component in the carbonic phase. The clathrate melting temperatures of Type II (L-L-V) inclusions and Type III vapor-rich inclusions range from 5.3◦ C to 9◦ C. It is noticeable that the depression of the clathrate melting temperature from 10◦ C is affected by the amount of salt in the aqueous phase in the Type II (L-L-V) inclusions. The salinity of aqueous fluid ranges from 1–7wt% NaCl eq. (Fig. 7) is obtained from the depression of clathrate melting temperature using the equation of Brown.10

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Fig. 7. Histogram shows salinity variation in Type II and Type III inclusions of quartz from Huai Kham On deposit. The majority of inclusions contain 2–3wt% NaCl equivalent.

Fig. 8. Histogram shows homogenization temperature variation of Type II and Type III fluid inclusions from quartz of Huai Kham On deposit.

The majority of final homogenization temperatures (Th ) of Type II (L-L-V) inclusions are 240◦ C to 310◦ C (Fig. 8), whereas the final homogenization temperatures of Type III vapor-rich inclusions are slightly higher (320◦ C–340◦C). The difference between the final homogenization temperatures of Type II and Type III inclusions may be due to a kinetic barrier during the formation of vapor and liquid phases of aqueous– carbonic fluids. Since the majority of inclusions were homogenized at temperature ranging from 280◦C to 300◦ C. These temperature ranges

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Fig. 9. Th -XH2 O plots for Type II and Type III inclusions. The data, which are scattered above 1 kb solvus, suggest that the inclusions were trapped at pressure above 1 kb.8,9

therefore, represented the homogenization of Type II and Type III fluid inclusions. The similarity between the final homogenization temperatures (Th ) of the liquid-rich and vapor-rich inclusions is, therefore, interpreted as the same trapped P –T conditions of 280◦ C to 300◦ C and 1 to 3 kb.

5.3. P–T–V–X relation Homogenization of Type II and Type III inclusions are plotted against molar volume of XH2 O (Fig. 9). The data plotted above the 1 kb solvus indicate that the pressure of entrapment of Type II inclusions and Type III inclusions were higher than 1 kb. It is also noticeable that the salinity– homogenization temperature plots form clusters rather than a mixing trend suggests that the ore-forming fluids were derived from homogeneous fluid (Fig. 10).

6. Gold Mineralization at Huai Kham On Deposit Gold grains that are present as cavity filling and as dissemination in quartz without observable fractures may suggest that they were formed simultaneously with the formation of quartz veins. Therefore, primary fluid inclusions in quartz veins were trapped simultaneously with the formation of gold mineralization.

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Fig. 10. Salinity — Homogenization plots showing composition of fluid inclusions of vein quartz from Huai Kham On deposit.

The presence of Type II (L-L-V) aqueous–carbonic and Type III vapor rich fluid inclusions in gold-bearing vein quartz in conjunction with microthermometric measurements indicate that gold-bearing quartz veins at the Huai Kham On deposit were derived from aqueous–carbonic fluids, which are composed of 2–3wt% NaCl equivalent in the aqueous phase, whereas CO2 is a major component in the carbonic phases. Evidence from fluid inclusion trapping temperature also indicates that the majority of oreforming fluids were trapped at 280◦ C–300◦C at 1 kb–3 kb. These pressure and temperature ranges are similar to those in mesothermal and/or orogenic gold styles of mineralization. The presence of aqueous–carbonic (Type II and Type III) fluid inclusions indicates that the precipitation of ore at the Huai Kham On deposit occurred by fluid effervescence process, which can be only formed under high pressure condition. Fluid effervescence (phase separation of CO2 ) may have resulted in pressure reduction.

7. Conclusions 1. Sulfur isotope characteristics suggest that the ore-forming fluids at Huai Kham On were formed either from magmatic fluids. 2. Fluid inclusion studies indicate that the gold-bearing quartz veins at the Huai Kham On deposit were formed at 280◦ C–300◦C and 1 kb–3 kb. 3. The formation of gold-bearing quartz veins resulted from fluid effervescence which led to rapid pressure reduction in the hydrothermal system.

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4. The pressure–temperature range of ore formation is similar to that of mesothermal or orogenic gold mineralization around the world.

References 1. S. Bunopas, Palaeogeographic history of western Thailand and adjacent part of Southeast Asia: A plate tectonic interpretation, PhD Thesis, Victoria University of Wellington, 1981, 810p. 2. S. Bunopas, P. Vella, H. Fontaine, S. Hada, C. Burrett, P. Haines, S. Potisat, Th. Wongwanich, P. Chaodamrong, K. T. Howard and S. Khositanont, Growth of Asia in the late Triassic continent–continent collision of Shan Thai and Indochina against South China, J. Gonwana Res. 4(4) (2001) 584–586. 3. S. M. Bar, S. A. Macdonal, G. R. Dunning, P. Ounchanum and W. Yaowanoiyothin, Petrochemistry, U-Pb (zircon) age and the palaeotectonic setting of the Lampang volcanic belt, northern Thailand, J. Geol. Soc. London 157 (2000) 553–563. 4. I. Metcalfe, Permian tectonic framework and palaegeography of SE Asia, J. SE Asian Earth Sci. 20 (2002) 551–566. 5. P. Chaodamrong, Stratigraphy, sedimentology and tectonic implications of the Lampang Group, central north Thailand, PhD Thesis, University of Tasmania, 1992, 230p. 6. B. W. Robinson and M. Kasakabe, Quantitative preparation of SO2 for 34 S/32 S analyses from sulphides by combustion with cuprous oxide, Anal. Chem. 47(7) (1975) 1179–1181. 7. H. Ohmoto and R. O. Rye, Isotope of sulphur and carbon, Geochem. Hydrothermal Ore Deposits (1979) 509–567. 8. E. Roedder, Fluid inclusion, Rev. Mineralogy 12 (1984) 646. 9. T. S. Bowers and H. C. Helgeson, Calculation of the thermodynamic and geochemical consequences of nonideal mixing in the system H2 O-CO2 -NaCl on phase relations in geologic systems: Equation of state for H2 O-CO2 -NaCl fluids at high pressure and temperatures, Geochim Cosmochim Acta 47 (1983) 1247–1275. 10. P. Brown, Flincor Software (1989).

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Advances in Geosciences Vol. 13: Solid Earth (2007) Ed. Kenji Satake c World Scientific Publishing Company


FORMATION OF HOLLOW CONCRETIONS IN NORTHEASTERN THAILAND PRINYA PUTTHAPIBAN∗ and SUTATCHA HONGSRESAWAT Geoscience Programme, Department of Physics, Faculty of Science, Mahidol University, Rama VI Road, Bangkok 10400, Thailand ∗ [email protected]

The mysterious rocks “Naka’s eggs” commonly found in Northeastern Thailand are hollow concretions derived from clastic rocks of the Khorat Group. The concretions appear in different shapes, such as spheroidal, ellipsoidal, and irregular with sizes varying from a few cm up to 60 cm. Their dark brown outer shells are much harder than the hosted rocks, and the inner surfaces of the hollows are rugged and occasionally contain remnants of pyrite (FeS2 ) minerals indicating incomplete oxidation processes. The result of extensive examinations of these hollow concretions suggests that their formation involves subsurface water that penetrates through fractures of rocks and the boundaries of sand grains forming several species of iron solutions. Due to their exothermic nature, these solutions sieve outward to the region with lower temperature and pressure where chemical reactions can continue. When equilibrium is reached, reddish brown iron oxide sediments remained as hard shells of the concretions. The hollow is then created in situ as a result of these chemical processes. The size and shape of these hollow concretions clearly depend on the quantity of pyrite crystals and the morphology of the pyrite nodules. As an external erosion process subsequently takes place, the outer shells which are more resistant and have a smaller porosity due to the secondary cemented iron oxides survive with shapes of sphere, ellipsoid and others, whereas other sandy parts of the host were eroded away. Because it is evidently clear that the reddish-brown color of the clastic rocks in our study areas is secondary in origin, parts of chemical reactions discussed here are promising candidates for actual chemical alterations responsible for the reddish color of the Khorat Group red beds in Thailand.

1. Introduction The continental Mesozoic rocks of the Khorat Group in northeastern Thailand have been intensively studied by many researchers.1−5 These rock ∗ Corresponding

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sequences vary in age from Upper Triassic to Cretaceous-Tertiary.6−9 The rocks of the Khorat Group are subdivided into nine formations listed from the oldest to the youngest as follows: Huai Hin Lat, Nam Phong, Phu Kradung, Phra Wihan, Sao Khua, Phu Phan, Khok Kruat, Maha Sarakham, and Phu Thok Formations.10,11 In this study, we present evidences of the formation of mysterious rocks, “Naka’s eggs” or hollow concretions which are found in the low hills with gentle slopes and flat land within the terrain covered by the clastic rocks of the Phra Wihan and Sua Khua Formations (Study Area 1) and of the Phu Kradung Formation

Fig. 1. Geologic map of Southeastern Khorat Plateau showing locations of the hollowed concretions Study Area 1 (filled circle) Phu Phrao near the Ban Chong Meg border town, Sirinthorn District, and Study Area 2 (open star in a filled circle) Ban Palai, Amphoe Po Sai, Ubon Ratchathani Province (filled star) Pak Moon Dam Site (modified after Sattayarak and Suteethorn, 1983).

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(Study Area 2), shown in Fig. 1. Consequently, we propose, for the first time, the mechanisms responsible for the creation of the hollow concretions.

2. General Geology of the Study Areas The Khorat Group clastic rocks in the southeastern corner of the northeastern plateau are generally flat lying beds, and slightly dip west toward the center of the Khorat basin (Fig. 1). The study areas predominantly contain three major rock formations. The first formation is classified as the Middle to Upper Jurassic Phu Kradung Formation normally distinguished by brown to grayish siltstones and fine-grained sandstones. This rock formation is mainly observed in the Study Area 2. Two additional formations observed in the Study Area 1 are Upper Jurassic to Lower Cretaceous Phra Wihan Formation containing mostly quartzitic sandstones and siltstones and the Lower Cretaceous Sua Khua Formation. This last formation is recognized as the rock unit where most vertebrate fossils were excavated in Thailand. Typical rocks in the Sao Khua ranging from most to least abundant are reddish brown sandstones, siltstones, conglomeratic sandstones, conglomerates, and intercalated shale, respectively.

3. Physical Descriptions of the Hollow Concretions Most spaces of hollow concretions’ cores are empty with minute amounts of loose sand, silt, or clay particles or detached hard lumps of clay materials resembling nuts. These concretions appear in various sizes and shapes. Their average sizes vary from a few cm up to 60 cm in lengths and greatly depend on the grain sizes of the pre-existing clastic rocks of the formation (Fig. 2). The typical shapes of these concretions are spheroidal, ellipsoidal, distorted cylindrical, and irregular (Fig. 3). While embedded in the strata, these hollow rocks do not show any uniform patterns of distribution. The outer shells are significantly harder than the surrounding rocks in which they are buried. When viewing the cross section, the inner part near the hollow has a reddish brown color, and such color fades outward making the outermost crust relatively pale. The inner surface of the hollows is usually rugged showing distinct corroded features. In some cases, there are remnants of pyrite crystals (FeS2 ) observed at the inner surfaces. We will explain in the next section that several species of iron solutions react as additional cementing media which hold the mantle of hollow concretions tighter than the sand grains farther away in the rock formations.

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Fig. 2. The sizes of the hollow concretions are larger in the coarser-grained clastic rocks comparing to those of the finer-grained one, (a), (b), and (c) show the concretions from the Study Area 1 and (d) shows the concretions from the Study Area 2.

Fig. 3. The adopted shapes of the hollow concretions showing their dependence on the pre-existing pyrite nodules. (a) and (b) are from the Study Area 1, and (c) and (d) are from the Study Area 2.

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4. Formation Processes Extensive examinations performed on hundreds of samples of the hollow concretions collected from and observed in the fields lead to the conclusion that the most suitable processes responsible for their formation are sedimentary depositions followed by a series of chemical reactions and ended with weathering and erosion processes. From our present information, the best formation scenario is chronologically described as follows. 4.1. Lithified stage The first stage is the formation of clastic sedimentary rocks, siltstone, sandstone, shale, and conglomerate present in the Phu Kradung, Phra Wihan, and Sao Khua Formations by meandering and braided stream deposits. The existence of pyrite (FeS2 ) occurring as individual crystals, clusters of combined crystals or pyrite nodules, the existence of coal jets and carbonaceous materials suggest that these rock formations were formed under significantly reducing conditions. These crystalline pyrites distribute unevenly throughout the rock formations. The gray to greenish gray color of the rock formations also indicates reducing environment of depositions. This color can only be observed when the rocks are not subjected to prior chemical weathering processes, fresh rocks. 4.2. Subsurface chemical reaction stage The second stage involves activities of subsurface water which sieves through fractures and grain boundaries of these pyrite-bearing rocks upon being exposed above the water table. The effectiveness of this water transmission is mainly regulated by the porosity and permeability of the rocks; therefore, properties such as grain size, primary rock structures, and the complexity of fracture system are important. The presence of water and oxygen gradually converts the system into oxidizing environment. As a result, several chemical reactions are triggered and often release heat and pressure to the system where reactions occur. The following four chemical equations show the general accepted sequence of pyrite reactions12,13 : + 2FeS2 + 7O2 + 2H2 O → 2Fe2+ + 4SO2− 4 + 4H

4Fe2+ + O2 + 4H+ → 4Fe3+ + 2H2 O 4Fe3+ + 12H2 O → 4Fe(OH)3 + 12H+ + FeS2 + 14Fe3+ → 15Fe2+ + 2SO2= 4 + 16H .

(1) (2) (3) (4)

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In Eq. (1), FeS2 which is stable under the previous reducing condition starts to react with oxygen and water to form ferrous iron (Fe2+ ), sulfate (SO2− 4 ), and acid (H+ ). Later, Fe2+ is transformed to ferric iron (Fe3+ ), as shown in Eq. (2). Fe3+ ions can undergo two different reactions, either hydrolyzed with water to form the solid ferric hydroxide (ferrihydrite) (Fe(OH)3 ) shown in Eq. (3) or further consume more FeS2 releasing additional + Fe2+ , SO2− 4 , and H , as shown in Eq. (4). These subsequent oxidation reactions of the pyrite crystals cause the textural failure; consequently, the individual sand grains (mainly quartz) previously held in the sedimentary rock matrix fall off becoming loose sand and silt particles. The Fe2+ /Fe3+ solution percolates outward seeking appropriate environments with lower temperature and pressure for chemical reactions to continue. This radiallyoutward percolation ceases at the outer shell of the concretion after the system reaches equilibrium leaving behind the rusty color of iron oxide. In the presence of alkaline minerals such as feldspars, the H+ and water from the above equations will be naturally neutralized by presumably, the following chemical reactions: 4KAlSi3 O8 + 4H+ + 18H2 O → 4K+ + 2Al2 Si2 O5 (OH)4 + 8Si(OH)4 2CaAl2 Si2 O8 + 4H+ + 2H2 O → 2Ca2+ + Al4 Si4 O10 (OH)8

(5) (6)

The end product of both Eqs. (5) and (6) is mainly a kaolinite clay. This clay mineral can further react with SiO2 and water yielding the solution of silica and silicic acid (H2 SiO4 ). These iron oxide and silicic solutions which disperse throughout the mantle additionally strengthen the crust of the hollow concretions. If carbonate minerals such as calcite are present, the neutralized process can be described by the following equations, and the chemical weathering product will be the mineral gypsum: CaCO3 + 2H+ + SO2− 4 + H2 O → CaSO4 · 2H2 O + H2 CO3

(7)

− CaCO3 + H+ + SO2− 4 + H2 O → CaSO4 · 2H2 O + HCO3

(8)

The embedded FeS2 crystals and nodules in the rock formations are evidently the essential starting materials for these chemical reactions to persist. Final size and shape of the hollow concretions are directly derived from the quantity of FeS2 and the orientation of the FeS2 nodules (Fig. 4).

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Fig. 4. Successive developments of the hollow concretions. (a) is gray to greenish gray, fresh sandstone with pyrite and carbonaceous materials of the Phra Wihan Formation excavating from the river bed near the Pak Moon Dam site; (b) shows the alteration of the similar rocks shown in (a) after being exposed to air for some years. Rusty spots and banded circles evidently ascertain the validity of chemical reactions caused by weathering; (c) shows the rugged inner surface of the hollow previously underwent severe chemical reactions; (d) shows trapped loose sand, silt, and clay particles which are always observed in the undisturbed hollows.

4.3. Post-chemical erosion stage The last stage is the prolongation of weathering and erosion processes undertaking well after maturation of chemical reactions of the previous stage. The portions of the rock farther away from the crust of the concretion are subjected to erosion process much more severely and are easily removed. Without surrounding materials, some hollow concretions become free and can be transported to new depositional sites by various means. The remaining partially free hollow concretions are left embedded in situ within the outcrop exposures (Fig. 5).

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Fig. 5. The outcrops containing partially free hollow concretions embedded in situ at Phu Phrao, the Study Area 1.

5. Conclusion The origin of hollow concretions can be summarized by the lithified stage, the subsurface chemical reaction stage, and the post-chemical erosion stage. The lithified process of the clastic rocks occurs under significantly reducing condition as indicated by the existence of pyrite nodules. Later on, the environment becomes more oxidized due to the presence of subsurface water and oxygen. Several chemical reactions take place forming hollows and strengthening outer shells of the concretions. The final post-chemical erosion stage explains the mobility and the distribution of the hollow concretions observed in the rock outcroups.

Acknowledgments Mahidol University, Faculty of Science and Mahidol University, Kanchanaburi campus are thanked for their support and encouragement. The authors are grateful for the provision of basic geological information

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by The Royal Thai Department of Mineral Resources. Sirot Sulyapongse, Manop Raksaskulwong, Apichat Lumjuan, and Nopadol Chaikum are thanked for offering valuable discussions. The provisions of research facilities and necessary supports from Nakorn Hamah, Vice President for Campus Development, Mahidol University and Tanakorn Osotchan, Director of the Physics Department, Faculty of Science, Mahidol University are appreciated.

References 1. T. Kobayashi, F. Takai and I. Hayami, On some Mesozoic fossils from the Khorat Series of East Thailand a note on the Khorat Series, Geol. Palaeontol. SE Asia 1 (1964) 119–133. 2. D. E. Ward and D. Bunnag, Stratigraphy of the Mesozoic Khorat Group in Northeast Thailand, Department of Mineral Resources of Thailand Report of Investigation 6 (1964) 95pp. 3. R. Ingavat and P. Taquet, J. Geol. Soc. Thailand 3 (1978) 1–6. 4. N. Sattayarak, Review of the continental Mesozoic stratigraphy of Thailand, Proc. Workshop on Stratigraphic Correlation of Thailand and Malaysia, ed. P. Nutalaya (Had Yai, Thailand, 1983), pp. 127–148. 5. E. Buffetaut and V. Suteethorn, The biogeographical significance of the Mesozoic vertebrates from Thailand, in Biogeography and Geological Evolution of Southeast Thailand, eds. R. Hall and J. D. Holloway (Backbuys, 1998), pp. 83–90. 6. A. Meesook, V. Suteethorn and T. Wongprayoon, Early Cretaceous nonmarine bivalves of the Sao Khua Formation, Khorat Group, Northeastern Thailand, 3rd Symposium IGCP 350, Manila, Philippines, Program and Abstract Volume (1995), pp. 10–11. 7. A. Meesook, V. Suteethorn, P. Chaodumrong, N. Teerarungsigul, A. Sardsud and T. Wongprayoon, Mesozoic rocks of Thailand: A summary, Proc. Symposium on Geology of Thailand, eds. N. Mantajit and S. Potisat (2002), pp. 82–94. 8. E. Buffetaut, V. Suteethorn, H. Tong, Y. Chaimanee and S. Khunsubha, New dinosaur discoveries in the Jurassic and Cretaceous of Northeastern Thailand, Proc. Int. Conf. Stratigraphy and Tectonic Evolution of Southeast Asia and the South Pacific (GEOTHAI 1997), eds. P. Dheeradilok, C. Hinthong, P. Chaodumrong, P. Putthapiban, W. Tansathien, C. Uthaaroon, N. Sattayarak, T. Nuchanong and S. Techawan, Vol. 1 (1997), pp. 177–187. 9. A. Racey, J. G. S. Goodall, M. A. Love, S. Polachan and P. D. Jones, New age data for the Mesozoic Khorat Group of Northeastern Thailand, Proc. Int. Symp. Stratigraphic Correlation of Southeast Asia, eds. P. Angsuwathana, T. Wongwanich, W. Tansathien, S. Wongsomsak and J. Tulyatid (Department of Mineral Resources, 1994), pp. 245–252.

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10. L. S. Gardner, H. F. Howarth and P. Na Chiangmai, Salt resources of Thailand, Department of Mineral Resources, Report of Investigation 11 (1967) 100pp. 11. N. Sattayarak and V. Suteethorn, Geological Map of Thailand 1:500,000 (Northeastern Sheet) (Geological Survey Division, Department of Mineral Resources, Thailand, 1983). 12. W. Stumm and J. J. Morgan, Aquatic Chemistry: An Introduction Emphasizing Chemical Equilibria in Natural Waters, 3rd edn. (John Wiley and Sons, New York, 1996). 13. K. B. Krauskopf, Introduction to Geochemistry, International Series in the Earth and Planetary Sciences (McGraw-Hill Book Company, 1967).

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Advances in Geosciences Vol. 13: Solid Earth (2007) Ed. Kenji Satake c World Scientific Publishing Company


INVESTIGATIONS ON LOCAL QUARTZ SAND FOR APPLICATION IN GLASS INDUSTRY∗

PISUTTI DARARUTANA The Royal Thai Army Chemical Department, Phaholyothin Road, Chatuchak, Bangkok, 10900 Thailand; The Glass and Glass Products Research and Development Laboratory, Institute for Science and Technology Research and Development, Chiang Mai University; The Graduate School of Chiang Mai University [email protected] PRUKSWAN CHETANACHAN National Institute of Health, Department of Medical Sciences, Nonthaburi 11000, Thailand PORNSAWAT WATHANAKUL Department of Earth Sciences, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand NARIN SIRIKULRAT Department of Physics, Faculty of Science, Chiang Mai University, Muang, Chiang Mai, 50200 Thailand; The Glass and Glass Products Research and Development Laboratory, Institute for Science and Technology Research and Development, Chiang Mai University

Silica or glass sand is a special type of quartz sand that is suitable for glassmaking, because of its high silica content, and its low content of iron oxide and other compounds. In Thailand, deposits of quartz sand are found as the beach and the river sands in many areas; eastern, southern, northeastern and northern. In this work, grain-size distribution and chemical analyses were carried out on 10 sand samples taken from various localities in Thailand such as Chanthaburi, Trat, Rayong, Chumphon, Nakhon Si, Pattani, Phuket, Songkhla, Nong Khai, and Tak provinces. The geological resources show that most of them are the surface-to-near-surface glass sand deposits. The sand grains in most deposits were mainly angular-to-rounded, except in some areas of either angular or rounded grains. Chemical analysis showed that the sands

∗ Work partially supported by The Graduate School of Chiang Mai University and The Office of the National Research Council of Thailand.

23

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contained more than 95wt% silica and low content of Fe, Al, Ca, Mg, Na, and K. The concentration levels of these components in the samples confirm with internationally acceptable standard for glass production. The quartz sand dressing plants that used the spiral classifier to improve the properties of the quartz sands to meet the standard specifications are mostly located in the eastern area. It can be concluded that most of the quartz sand deposits in Thailand investigated show well-sorted grain-size with considerable purity, i.e. high-grade quality. The advanced works resulted in that these raw quartz sands can be used as raw material for fabrication of soda-lime, lead crystal, and lead-free high refractive index glasses. The colorless and various colored glass products have been satisfactorily used in the domestic art and glass manufactures.

1. Introduction Glass is one of the oldest artificial materials known to man. The major raw materials of the most common glasses are the same as the oldest known glass recipe, and the process of glass making was recorded on clay tablets from the Royal Library of Assur-bani-pal at Nineveh (Iraq) in the seventh century BC. Silica, lime, and alkali are the bases of oxide glasses, which are of great importance, both historically and technologically. The source of silica is quartz sand.1−3 Sand consists of small grains or particles of minerals and rock fragments. Although these grains may be of any mineral composition, the dominant component of silica sand is the mineral quartz. Glass sand is a special type of sand that is suitable for glass making because of its high silica content and its low conduct of iron oxide and other compounds. It may be produced from both unconsolidated sands and crushed sandstones.4 The glass sand standards that was fixed by the US Bureau of Standard fixed the Sand Class Conditions for glass making,5 and the British standard methods for sampling and analyzing of glassmaking sand6 (Table 1). In Thailand, deposits of glass sand are found to be of both the beach and the river sands; most of them are beach sand,8 located in many areas; eastern, southern, northeastern, and northern.7 The geological Table 1. Standard

USA British

The standard of glass sand. Composition (%wt)

SiO2

Fe2 O3

Al2 O3

CaO + MgO

95 (min) 98.5 (min)

1 (max) 0.30 (max)

4 (max) —

0.5 (max) —

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resources show that they are mainly the surface-to-near-surface glass sand deposits. The previous works indicated that the other local raw materials including zircon sand and basalt rock also met the requirements for the glass industrial process.9,10 The local quartz sand from Trat area was satisfactory for the lead crystal glass fabrication.11 In this work, the physical structure, grain-size distribution, and chemical analyses of quartz sand samples taken from various localities in Thailand were investigated.

2. Experiment The geological explorations of silica sand were carried out in 10 various localities in Thailand such as Chanthaburi, Trat, Rayong, Chumphon, Nakhon Si, Pattani, Phuket, Songkhla, Nong Khai, and Tak provinces. The sand samples of about 10–100 k from each locality were taken to analyze their properties. The samples were prepared using standard metallographic technique prior to other specific experimental investigations. The physical structure investigations of the samples were determined using scanning electron microscope (SEM); Jeol JSM-5910, operated at 20 kV. The chemical compositions were determined by the wavelengthdispersive X-ray fluorescence spectrometer (WDXRF), Phillips MagixPro PW 2400, operated with LiF 200 crystal, scintillation and flow proportional detectors, with the Rh-tube at 60 kV 125 mA. The neutron activation analysis (NAA) was used to determine the trace elements. The grain distributions of the sand samples from each locality were analyzed by using the particle size analyzer, Malvern Instrument Mastersizer, using polydisperse analysis, and operated with a beam length of 240 mm and range lens 300 RF mm.

3. Results and Discussion The geological explorations showed that most of the sand resources from 10 localities in Thailand (Chanthaburi, Trat, Rayong, Chumphon, Nakhon Si, Pattani, Phuket, and Songkhla provinces) were the surfaceto-near-surface glass sand deposits, except at Nong Khai site that was the subsurface deposit. Most of them were the beach sand, except that of Nong Khai site that was the river sand, and at Tak deposit was the by-product from the feldspar floatation plant. The beach sands were

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Fig. 1.

The micrographs of the local sands studied.

deposited as lenses, of more than 100 m2 and approximately 0.2–2.0 m thick each, covering the wide area possibly about 1–3 km2 in each locality. Scanning electron microscope investigation (Fig. 1) revealed that the grain shape of the older beach sands (Chanthaburi, Trat, and Pattani) were angular to rounded. The present beach sands of Rayong, Chumphon, and Nakhon Si, showed the angular grain, whereas the Songkhla and Phuket sites were the rounded and the angular-to-rounded grains, respectively. The river sand from Nong Khai site was the angular-to-round grain, mixed with

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calcium carbonate in composition. The smaller grain sizes and angular grain were found at Tak site. Most of the older beach sand showed fine-grain, and also most of the present beach sand, except the Nakhon Si site showed fine to coarse grain. The fine grain was founded from Nong Khai and Tak sites. The color of the older beach sand was pink–gray, except the one from Rayong site that was orange–gray. Most of the present beach sands were pink–gray, but those at Phuket site were gray in color. Both the river sand from Nong Khai and the feldspar-floated sand from Tak sites showed white color. The origin, grain shape, and color of the local sands are shown in Table 2. Results of the grain-size analysis showed that the sizing of the sand particles was between 20 to 510 µm, as shown in Table 3, that fall within the ideal sand fraction range used for the glass production. Chemical analysis showed that the sands contain more than 95wt% silica and low content of Fe2 O3 , Al2 O3 , CaO, MgO, Na2 O, and K2 O, as shown in Table 4. Because of its origin of occurrences, the quartz glass sand from Tak site contained high silica and low impurities due to being the product of feldspar floatation. The concentration silica and the minor components in most of the samples confirmed with the internationally acceptable standards for glass production. Moreover, the trace elements using neutron activation analysis showed the presence of Hf, Os, Ti, Yb, and Se in the beach sand. All of these differential properties may be caused from the chemical and the physical weathering, and the biotic effect that erode the original source rocks. The quartz sand dressing plants that used the spiral classifier to improve the properties of the quartz sands to meet the wanted specifications were mostly founded in the eastern area. Table 2. Sites Rayong Chanthaburi Trat Chumphon Songkhla Phuket Pattani Nakhon Si Nong Khai Tak

The origin, grain shape, and color of the local glass sands. Origin

Grain shape, texture

Older beach Older beach Present beach Present beach Present beach Present beach Old beach Present beach River By-product

Angular-to-rounded, fine Angular-to-rounded, fine Angular, fine Angular, fine Rounded, fine Angular-to-rounded, fine Angular-to-rounded, fine Angular, fine-to-coarse Angular-to-rounded, fine Angular, fine

Color Orange–gray Pink–gray Pink–gray Pink–gray Pink–gray Gray Pink–gray Pink–gray White White

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The distribution of local quartz sands. Grain size, µm

Sites D(V , 0.1)

D(V , 0.5)

D(V , 0.9)

132 138 168 150 140 201 155 54 185 20

191 250 248 219 222 331 241 224 252 38

264 395 344 308 322 510 349 477 423 139

Rayong Chanthaburi Trat Chumphon Songkhla Phuket Pattani Nakhon Si Nong Khai Tak

Table 4.

Specific area m2 /g

0.148 0.138 0.166 0.146 0.119 0.212 0.140 0.118 0.158 0.026

0.043 0.057 0.046 0.043 0.055 0.040 0.050 0.050 0.031 0.474

The composition of local quartz sands.

Sites

Rayong Chanthaburi Trat Chumphon Songkhla Phuket Pattani Nakhon Si Nong Khai Tak

Concentration %vol

Composition SiO2

Fe2 O3

Al2 O3

CaO + MgO

Na2 O + K2 O

97.5 98.8 98.8 99.1 96.9 97.0 96.0 95.2 97.6 99.2

0.31 0.05 0.05 0.03 0.12 0.37 0.10 0.70 0.14 <0.01

0.95 0.77 0.56 0.57 1.85 0.85 2.60 1.65 1.24 0.36

0.03 0.01 <0.01 <0.01 0.01 0.01 0.60 0.13 0.03 <0.01

0.14 0.22 <0.01 0.02 0.57 0.03 <0.01 1.35 <0.01 0.20

4. Conclusion It can be concluded that the investigations on the quartz sand deposits in Thailand were very useful; the results showed that most of them were of well-sorted grains with considerable purity with high silica content, i.e. high-grade quality. The consequent works have revealed that these raw quartz sands can be used as raw materials for fabrication of soda-lime, lead crystal, and lead-free high refractive index glasses for the colorless and various colored glass products, and found to be satisfactory for the art and domestic glass manufacturers.12−14

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Acknowledgments Uses of the scanning electron microscope and the wavelength-dispersive X-ray fluorescence spectrometer were supported by the Faculty of Science, Chiang Mai University at Chiang Mai Province. The particle size analyzer was supported by the Northern Ceramics Development Center at Lumpang Province. The Glass and Glass Products Research and Development Laboratory, Institute for Science and Technology of Chiang Mai University jointly supported the experiments of glass fabrication in the laboratory scale.

References 1. F. L. Harding, Introduction to Glass Science (Plenum Press, 1972). 2. S. Frank, Glass and Archaeology (Academic Press, 1982). 3. K. P. Kursula, PIXE and SEM Study of Old Finnish and European Glass and European Oyster Ostrea edulis, University of Helsinki, 1999. 4. Baumgart et al., Process Minerology of Ceramic Materials (Elsevier, 1984). 5. Glass Production Guide: Production of the US Glass Association (1965). 6. BS 2975 British Standard Methods for Sampling and Analyze of Glassmaking Sand (1988). 7. P. Ratanajaroorak, Glass sands, Department of Mineral Resources of Thailand (2005). 8. B. Jarooglas, Glass sands, Dep. Mineral Res. Econ. Geol. Bull. 35 (1982) 1–60. 9. P. Pimkhaokham and R. Conradt, Potential utilization of different local raw materials, Chulalongkorn University (1990). 10. P. Pimkhaokham et al., Raw material substitution in technical glass melting, Chulalongkorn University (1990). 11. K. Visetchat et al., Abstracts of the 29th Congress on Science and Technology of Thailand (2003), p. 202. 12. C. Iamjitkusol et al., Fabrication of glass for art’s work, Office of the National Research Council of Thailand (2004). 13. P. Dararutana and N. Sirikulrat, J. Micros. Soc. Thailand 20 (2006) 1. 14. P. Dararutana and N. Sirikulrat, KMITL Sci. J. 6 (2006) 2b.

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Advances in Geosciences Vol. 13: Solid Earth (2007) Ed. Kenji Satake c World Scientific Publishing Company


GEOCHEMICAL AND Sr–Nd–Pb ISOTOPIC STUDY OF LATE NEOGENE VOLCANIC ROCKS FROM THE ARITA–IMARI AREA (SW JAPAN): EVIDENCE FOR COEXISTING OIB-LIKE AND SUBDUCTIONRELATED MANTLE SOURCES NGUYEN HOANG∗ , JUN’ICHI ITOH† , KOZO UTO and AKIKAZU MATSUMOTO Geological Survey of Japan, AIST, Higashi 1-1-1 Tsukuba Central 7th, Tsukuba, Japan 305-8567 ∗ [email protected] † [email protected]

New geochemical and isotopic (Sr, Nd, Pb) data are reported for Neogene basaltic to high-K andesitic, dacitic, and rhyolitic lavas from four closely spaced volcanic centers in the back arc region of the Arita–Imari area, NW Kyushu, Japan. Two major eruptive groups are recognized: (1) 6.7 Ma small volcanoes such as Kuro-dake and Jinroku-yama comprise basaltic to andesitic stratocones that form a discreet volcanic edifice; (2) the 2.7–2.3 Ma Koshidake and Arita volcanic complex consisting of basaltic to high-silica volcanic products, erupted in the form of multiple vents. Andesitic types comprise about 90 vol.% of the erupted lavas, overall. The basalts conform broadly to oceanic island basalt (OIB) type, characterized by relatively high MgO (ca. 6wt%), low SiO2 (ca. 48.5wt%), and “smooth” (high field strength element-rich: HFSE-rich) mantle-normalized trace element patterns. They are isotopically enriched, with 87 Sr/86 Sr ranging 0.7040–0.7048, 143 Nd/144 Nd, 0.5128–0.5127, and Pb isotopes ranging between enriched mantle types EM1 and EM2like indicating small-scaled source heterogeneity. The Sr and Nd isotopic compositions of the andesite and rhyolite are even more enriched (0.7044– 0.7052, 0.51270–0.51267, respectively), however, within the range of northern Kyushu Cenozoic basalts. Chemically they fall in the calc-alkaline field and show strong HFSE-negative anomalies in mantle-normalized trace element patterns. Two chemically distinct groups are identified among the rhyolites, associated with (a) Arita and (b) Koshi-dake. Distinguishing geochemical parameters of each are, respectively as follows: (La/Yb)N (a) between 12 and 14.5, and (b) < 7; Ba/Nb (a) from 25 to 40, and (b) < 15; Rb/Sr (a) < 0.5, and (b) > 4.5; 87 Sr/86 Sr (a) 0.7047, and (b) 0.7052. The combined presence of relatively high 87 Sr/86 Sr isotope ratios and negative HFSE anomalies may be interpreted in terms of at least three potential effects: partial melting of, or magmatic assimilation by, lithospheric mantle and/or crustal material, contamination of the magmatic source by subducting slab-derived hydrous

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fluids, and the presence of refractory HFSE-rich phases during magma genesis and/or fluid segregation from the slab. The geochemical and isotopic characters of the Arita andesite–rhyolite series strongly suggest a combined history of assimilation–fractionation crystallization (AFC). On the other hand, the Koshidake rhyolites, while showing even higher 87 Sr/86 Sr ratios, exhibit significantly lower abundances of incompatible elements in general, and higher contents of heavy rare earth elements (HREE) as compared to their accompanying basalts, not readily explained via simple fractional crystallization or AFC processes.

1. Introduction Highly silicic magmas may be derived from mantle-equilibrated parent magmas through a variety of differentiation processes,1 including the assimilation of crustal material in association with fractional crystallization (AFC),2,3 or partial remelting of trapped andesitic or basaltic intravolcanic bodies in the middle or upper crust.4,5 Detecting the involvement of crustal materials is relatively easy given its distinctive, enriched character in terms of LILE, LREE, and radiogenic Sr and Pb isotopic characters.6 However, recognition of the more subtle distinctions between mantlederived signatures is handicapped by the superimposed effects of such intracrustal fractionation effects that may produce similar chemical and isotopic characteristics. In any case, the recognition of potential crustal contamination sources in magmas is an essential prerequisite to any successful petrogenetic model for SiO2 -rich magmas and, in turn, to interpreting the chemical evolution of continental crust through time. In northern Kyushu, southwestern Japan, Neogene intraplate volcanic activity produced widely scattered, mainly basaltic centers.7−11 The activity was sporadic, quiescent episodes being interpreted to correlate with renewed lithospheric extension triggered by plate-kinematic adjustments following the opening of the Japan Sea (20–14 Ma).12 Most workers have concluded that the dominant controls on the geochemistry of the intraplate basalts were related to their spatial and, to some extent, temporal distribution, reflecting both the geochemical evolution of their source regions and dominant intracrustal fractionation mechanisms.8,9,11 Abundant silicic (SiO2 > 75wt%) and andesitic eruptive products, also accompanying the basalts,7 either as a calc-alkaline continuum (NishiSonogi, Iki island, etc.) or as bimodal mafic — acidic associations (Saga– Futagoyama10). Early Pliocene to Pleistocene rhyolite and andesite complexes, along with subsidiary basalts, appear within an area of <100 km2 in the Arita– Imari area, in the eastern part of Kita-Matsuura, representing the largest

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region of intraplate volcanism in northwestern Kyushu post-dating opening of the Japan Sea.7,13,14 Their volcanic products form distinctive mountain peaks, overlying, and rising above the Tertiary sedimentary basement. From north to south, these include a bimodal association of basalt and rhyolite (2.7–2.6 Ma), in the vicinity of Koshi-dake, basalt and andesite

Fig. 1. Location map of study area: (a) Geological scheme of the Arita–Imari area; (b) Simplified from Ref. 13. Numbers are Ar/Ar ages. Quaternary volcanic front (dashed line in (a)) is also shown for reference.

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(5 Ma), at Kuro-dake, Sera-san, in the Arita complex — comprising 2.4– 2.3 Ma andesite, rhyolite, pyroclastics, along with a minor amount of basalt, and association of basalt and basaltic andesite, aged 6.8 Ma, in the Jinroku-yama area15,16 (Fig. 1). With the exception of the large-volume Arita volcanic complex (3 km3 in area), the other bodies do not exceed 0.5 km3 in area. The acidic to intermediate rocks typically consist of biotite rhyolite, hornblende-clinopyroxene dacite, olivine clinopyroxene andesite, and a range of biotite-hornblende dacite to rhyolite. Also encountered are aphyric andesites, rhyolites, obsidian, and (subsidiary) perlite. Xenoliths of basalt and andesite commonly occur in the silicic lavas and pyroclastic of the Arita complex. Petrographic studies of these show that some of the former retain fluid-like morphologies, along with the evidence of magma co-mingling and solid-melt reaction in the basalt inclusions and their host rhyolites (Fig. 2).

Fig. 2. Micro-photograph of a thin section showing shape and marginal contact of mafic inclusions in rhyolitic lavas in the Arita volcanic complex. (See text for detail.)

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A large sample set, including the relatively rare basalts and more abundant andesites and rhyolites, was collected from four of the Arita– Imari centers, for K–Ar age dating, along with petrologic, geochemical, and Sr–Nd–Pb isotopic studies. The objective of the study is to better define regional magmatic evolution patterns and interpret these in terms of magmatic source heterogeneity and possible intracrustal fractionation effects within a well-defined spatial–temporal framework. 2. Analytical Techniques The analytical work was conducted at the Geological Survey of Japan (GSJ). Procedures for X-ray fluorescence (XRF) major elements analyses have been described elsewhere,9,11 reported accuracies being better than ±1.5% based on repeated measurements of GSJ standard JB-1a. Trace elements were analyzed by inductively coupled plasma mass spectrometry (ICP-MS) on a Micromass Platform-ICP. Details of this procedure were reported by Hoang and Uto.17 ICP-MS running parameters are similar to those reported previously,18 reproducibility being better than ±4% (2σ) for rare earth elements, Rb, and Nb, and better than ±6% for other elements. For the analysis of Sr, Nd, and Pb isotopic compositions, 1–2 mm chipped samples were cleaned ultrasonically for about 30 mins, then dried and ground in an agate mill. All the acids and the water used during chemical work were certified TAMA-Pure AA-10 grade. Approximately, 50 mg aliquots of powder were dissolved in concentrated HNO3 and HF (ratio 1:2), re-dissolved in HNO3 before being evaporated. Sr-spec resin R from Eichrom
was used in Sr and Pb extractions, following procedures described by Deniel and Pin.19 Preliminary extraction of rare earth elements (REE) was accomplished using conventional AG50W-X8 resin in small quartz columns, Nd being extracted using Ln-resin (Eichrom) and 0.25 N HCl as eluant, following procedures described by Pin and Zalduegui.20 Nd, Sr, and Pb isotopic ratios were measured in a multicollector VG Sector 54 thermal ionization mass spectrometer (TIMS) at the GSJ. Details of TIMS running parameters were reported by Hoang and Uto.9,21 The within-run precision (2σ) for 87 Sr/86 Sr was about ±0.000007 and about ±0.000008 for 143 Nd/144 Nd. The internal precision of Pb isotopic ratios (2σ) is less than 0.01%, and total blank is less than 80 pg. The data are shown in Table 1.

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Chemical and Sr, Nd, and Pb isotopic compositions of the Arita–Imari lavas.

Sample Location

NAG02003 Koshi-dake

NAG02001 Koshi-dake

Age (m.y.) SiO2 TiO2 Al2 O3 FeO* MnO MgO CaO Na2 O K2 O P2 O5 Sum Mg # Zn Cu Ni Cr V Rb Sr Y Zr Nb Ba La Ce Pr Nd Sm Eu Gd Tb Dy Ho Er Tm Yb Lu Hf Ta Pb Th U 87 Sr/86 Sr 143 Nd/144 Nd εNd 206 Pb/204 Pb 207 Pb/204 Pb 208 Pb/204 Pb

2.61 48.94 2.66 16.57 11.96 0.18 5.97 8.37 3.43 1.39 0.53 100 47.07 101.6 38 75.4 157.3 233.1 28.8 443.87 31.23 175.37 31.49 354.75 24.6 50.41 6.05 27.74 6.62 2.18 6.68 1.04 5.74 1.16 2.99 0.46 2.66 0.39 4.21 2.01 3.29 2.77 0.6 0.704111 0.51278 2.77 17.87 15.438 38.107

2.6 53.58 1.34 14.79 8.26 0.15 7.85 9.5 2.78 1.46 0.28 100 62.88 102.5 39.8 72 147.7 226.9 30.69 444.89 33.94 174.42 30.66 367.32 23.52 49.69 6.11 26.93 6.45 2.2 6.48 1.01 5.66 1.12 3.09 0.45 2.62 0.39 4.01 1.91 2.95 2.71 0.58 0.704113 0.512774 2.65 17.924 15.488 38.238

NAG02004A Koshi-dake 2.71 76.43 0.03 13.09 1.16 0.05 0.03 0.64 3.99 4.56 0.02 100 4.47 41.3 1.6 32.8 118.9 178.35 45.07 25.13 71.88 20.17 228.99 23.36 42.65 4 13.6 3.11 0.22 3.39 0.6 3.92 0.83 2.31 0.37 2.27 0.33 2.98 2.7 23.53 16.31 4.78 0.705249 0.512691 1.03 18.346 15.582 38.575

NAG02022 Arita 2.4 48.75 2.68 16.72 12.04 0.19 5.74 8.37 3.52 1.46 0.54 100 45.94 102.4 40.4 62.5 111.7 230.8 31.54 470.99 32.18 175.76 30.34 368.23 24.11 50.41 6.17 27.94 6.74 2.28 6.43 1.03 5.72 1.13 3.04 0.42 2.59 0.4 4.15 1.53 2.3 2.54 0.53 0.704122 0.51275 2.18 17.956 15.515 38.32

NAG02018 Arita 2.4 57.14 1.32 16.29 8.11 0.17 3.56 7.06 3.7 2.29 0.36 100 43.94 78.4 30.2 73.7 266.7 140.9 84.29 381.52 27.23 200.96 24.45 485.55 27.24 52.17 5.54 22.3 4.61 1.36 4.33 0.68 4.16 0.84 2.28 0.35 2.14 0.33 4.51 1.76 7.99 6.67 1.6 0.70458 0.512699 1.19 18.287 15.576 38.548 (Continued)

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(Continued)

Sample Location

NAG02013 Arita

NAG02014 Arita

NAG02007 Arita

NAG02021 Arita

Age (m.y.) SiO2 TiO2 Al2 O3 FeO* MnO MgO CaO Na2 O K2 O P2 O5 Sum Mg # Zn Cu Ni Cr V Rb Sr Y Zr Nb Ba La Ce Pr Nd Sm Eu Gd Tb Dy Ho Er Tm Yb Lu Hf Ta Pb Th U 87 Sr/86 Sr 143 Nd/144 Nd εNd 206 Pb/204 Pb 207 Pb/204 Pb 208 Pb/204 Pb

2.4 58.53 1.13 16.26 7.3 0.13 3.46 6.74 3.72 2.45 0.26 100 45.82 76.8 24.4 88.7 396.2 138.3 69.45 345.91 24.14 215.43 22.41 487.2 30.21 54.87 5.64 22.29 4.54 1.41 4.48 0.71 4.06 0.84 2.42 0.37 2.23 0.35 4.88 1.62 10.54 7.49 1.61 0.704466 0.512706 1.32 18.288 15.58 38.546

2.4 58.44 1.16 16.33 7.28 0.13 3.55 6.79 3.65 2.41 0.27 100 46.53 79.7 24.3 89.3 413 139.5 70.21 357.54 24.43 219.48 23.61 500.01 29.45 53.93 5.66 22.04 4.57 1.42 4.27 0.67 3.8 0.8 2.37 0.32 2.09 0.33 4.79 1.25 8.11 6.57 1.59 0.704454 0.512691 1.04 18.285 15.57 38.53

2.4 60 1.23 17.21 6.71 0.14 2.27 5.31 4.5 2.25 0.38 100 37.65 91.7 15.6 52.1 228.2 125.6 61.79 515.32 30.36 263.09 25.99 543.64 41.19 77.7 8.92 32.37 6.32 1.81 5.65 0.85 4.81 0.95 2.63 0.38 2.48 0.39 6.05 1.34 10.22 5.7 1.26 0.704664 0.512678 0.77 18.341 15.583 38.563

2.4 61.63 1.34 16.74 4.87 0.1 2.5 5.73 3.94 2.75 0.39 100 47.75 94.8 26.5 31.9 118.7 134.5 76.1 588.63 22.89 193.87 24.72 467.05 36.89 75.31 8.2 32.42 6.35 1.61 5.05 0.74 3.74 0.73 1.96 0.29 1.85 0.29 4.86 1.4 11.97 8.06 1.71 0.705121 0.512682 0.86 18.347 15.576 38.555

NAG02015 Arita 2.4 63.37 0.89 15.56 5.34 0.1 2.39 5.18 3.8 3.16 0.22 100 44.36 71 16.3 52.9 228.5 96.9 88.16 282.75 23.48 198.49 21.59 512.75 31.67 55.01 5.51 20.9 4.08 1.21 3.94 0.62 3.63 0.74 2.12 0.33 1.99 0.3 4.33 1.68 13 10.18 2.19 0.704506 0.512715 1.51 18.307 15.578 38.54 (Continued)

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Table 1.

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Sample Location

NAG03202 Seira-san

NAG03211 Seira-san

NAG03207 Seira-san

NAG02009 Arita

Age (m.y.) SiO2 TiO2 Al2 O3 FeO* MnO MgO CaO Na2 O K2 O P2 O5 Sum Mg # Zn Cu Ni Cr V Rb Sr Y Zr Nb Ba La Ce Pr Nd Sm Eu Gd Tb Dy Ho Er Tm Yb Lu Hf Ta Pb Th U 87 Sr/86 Sr 143 Nd/144 Nd εNd 206 Pb/204 Pb 207 Pb/204 Pb 208 Pb/204 Pb

2.4 59.38 1.28 17.32 7.41 0.09 1.86 5.52 4.63 2.12 0.39 100 30.85 100.4 19.5 6.4 128.5 6 52.71 529.55 28.65 250.97 24.36 537.97 41.16 78.23 8.84 34.16 6.39 1.88 6.15 0.91 5.02 0.99 2.82 0.42 2.61 0.39 5.89 1.68 10.79 6.23 1.26 0.704686 0.512667 0.56 18.34 15.579 38.561

2.4 66.05 0.82 15.1 5.39 0.06 1.67 4.01 3.69 3.07 0.12 100 35.57 74.3 14 9.6 78 119.7 101.14 201.35 27.71 137.7 15.06 442.12 35.38 54.12 6 22.33 4.38 0.93 4.63 0.74 4.15 0.83 2.41 0.36 2.24 0.33 3.41 1.45 15.27 10.65 2.2 0.704968 0.51267 0.63 18.392 15.615 38.687

2.4 68.62 0.54 15.72 3.88 0.04 0.67 2.6 4.13 3.68 0.11 100 23.61 55.5 12 10.2 50.8 14.2 120.56 225.49 28.37 214.7 18.73 632.85 57.01 91.21 9.31 32.49 5.56 1.21 5.06 0.76 4.2 0.85 2.46 0.39 2.28 0.35 4.44 1.71 18.65 14.52 2.98 0.704754 0.512667 0.57 18.358 15.572 38.576

2.32 71.22 0.44 16.25 1.39 0.01 0.17 1.93 4.12 4.33 0.14 100 18.04 43.8 6.7 28.3 106.9 16.9 148.28 214.08 23.13 306.61 23.96 708.08 43.81 75.66 7.51 27.03 4.89 1.08 3.98 0.6 3.45 0.75 1.86 0.27 2.07 0.23 4.68 2.08 18.63 15.77 3.63 0.704834 0.512672 0.67 18.379 15.59 38.606

NAG02011 Arita 2.4 76.55 0.05 13 1.14 0.04 0.02 0.7 3.71 4.77 0.02 100 3.17 35.9 0.6 7.6 22.5 — 133.54 62.37 18.21 85.94 13.83 496.77 34.33 60.42 5.52 18.2 3.16 0.36 2.65 0.42 2.6 0.55 1.5 0.24 1.61 0.25 2.99 1.9 20.91 16.35 3.53 0.705133 0.512674 0.7 18.361 15.581 38.574 (Continued)

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(Continued)

Sample Location

NAG02005 Arita

NAG02028 Kuro-dake

NAG02026 Kuro-dake

NAG02025 Kuro-dake

NAG9301 Kuro-dake

Age (m.y.) SiO2 TiO2 Al2 O3 FeO* MnO MgO CaO Na2 O K2 O P2 O5 Sum Mg # Zn Cu Ni Cr V Rb Sr Y Zr Nb Ba La Ce Pr Nd Sm Eu Gd Tb Dy Ho Er Tm Yb Lu Hf Ta Pb Th U 87 Sr/86 Sr 143 Nd/144 Nd εNd 206 Pb/204 Pb 207 Pb/204 Pb 208 Pb/204 Pb

2.4 74.51 0.15 14.33 1.7 0.01 0.11 0.85 3.83 4.5 0.02 100 10.44 48.5 2.9 16.1 62.4 2.1 143.97 90.53 19.01 171.36 17.42 654.78 44.45 67.51 6.37 20.6 3.42 0.44 2.88 0.44 2.51 0.53 1.54 0.24 1.69 0.26 4.52 1.45 21.71 12.3 3.16 0.705139 0.512673 0.68 18.374 15.59 38.606

5 51.81 1.31 18.3 8.65 0.13 4.95 10.64 3.03 0.91 0.26 100 50.52 85.7 42.9 38 113.2 271.2 17.93 721.51 23.63 105.62 8.71 272.02 18.99 39.31 4.67 20.18 4.28 1.31 3.97 0.65 3.76 0.77 2.1 0.32 1.91 0.29 2.61 0.58 4.04 3.31 0.67 0.703903 0.512792 3 18.186 15.507 38.29

5 54.04 1.9 17.66 8.62 0.13 4.07 8.75 3.16 1.27 0.4 100 45.68 94.1 43 62.5 164 232.7 28.22 545.26 26.1 180.72 20.51 277.79 27.29 56.5 6.57 28.99 5.97 1.86 5.33 0.84 4.35 0.88 2.39 0.35 2.01 0.31 4.36 1.02 6.18 4.08 0.74 0.704871 0.512682 0.87 18.337 15.568 38.584

5 55.44 1.5 16.22 10.82 0.14 2.89 5.92 4 2.46 0.62 100 32.25 121.4 29.9 63.2 161 86 72.46 378.14 36.6 302.69 45.9 633.19 41.51 87.18 9.87 40.32 8.06 2.6 7.52 1.11 6.31 1.23 3.36 0.5 2.92 0.44 6.79 2.97 8.76 7.78 1.88 0.704917 0.512645 0.14 18.335 15.565 38.708

5 54.72 2.17 17.87 7.95 0.13 3.91 7.98 3.72 1.25 0.31 100 46.68 91 27 38.1 218 26.1 19 665 21 171 19 402 23.36 48.2 — 21.7 5.21 1.67 — 0.83 — — — — 1.85 0.23 4.08 1.34 — 3.02 1.19 0.70454 0.512738 1.95 18.362 15.566 38.572 (Continued)

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Table 1.

(Continued)

Sample Location

NAG02024 Kuro-dake

NAG02032 Jinroku-yama

NAG02029 Jinroku-yama

Age (m.y.) SiO2 TiO2 Al2 O3 FeO* MnO MgO CaO Na2 O K2 O P2 O5 Sum Mg # Zn Cu Ni Cr V Rb Sr Y Zr Nb Ba La Ce Pr Nd Sm Eu Gd Tb Dy Ho Er Tm Yb Lu Hf Ta Pb Th U 87 Sr/86 Sr 143 Nd/144 Nd εNd 206 Pb/204 Pb 207 Pb/204 Pb 208 Pb/204 Pb

5 57.56 1.35 15.59 7.83 0.14 4.74 6.7 3.37 2.4 0.32 100 51.89 105.8 36.7 127.6 384.8 151.6 134.86 339.15 37.2 165.89 43.33 284.4 26.15 53.13 6 25.29 5.81 1.34 6.01 1.06 6.11 1.29 3.49 0.54 3.16 0.44 4.59 4.05 16.24 10.86 3.11 0.704638 0.512687 0.96 18.37 15.568 38.71

6.77 50.45 2.32 15.88 11.72 0.16 5.13 10.16 2.95 0.91 0.3 100 43.84 110.3 43.2 149.7 327.2 182.9 26.27 389.06 28.51 155.68 23.5 296.09 18.3 39.6 4.83 21.75 5.18 1.73 5.36 0.84 4.88 0.97 2.51 0.36 2.01 0.3 3.67 1.49 1.65 2.42 0.5 0.704125 0.512765 2.47 17.988 15.493 38.673

6.77 56.14 1.08 15.8 7.66 0.12 5.59 9.07 2.91 1.44 0.18 100 56.55 72.9 32.5 68.8 329.2 215.6 42.7 368.08 24.48 115.03 12.82 341.64 20.84 35.57 4.43 19.38 4.24 1.37 4.07 0.7 3.78 0.8 2.15 0.34 2.09 0.33 2.85 0.78 5.32 4.55 1.02 0.704439 0.512722 1.63 18.297 15.553 38.55

NAG02030 Jinroku-yama 6.77 56.69 1.12 14.78 8.41 0.13 7.06 7.4 2.97 1.29 0.15 100 59.94 78.4 34.4 139.9 503.6 155.6 38.82 260.41 21.07 108.8 9.96 283.25 14.13 27.45 3.15 13.07 3.22 1.04 3.53 0.56 3.42 0.72 1.95 0.31 1.8 0.28 2.63 0.82 4.39 4.36 0.94 0.704141 0.512784 2.85 18.289 15.55 38.525

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3. Analytical Results 3.1. Major element compositions Phenocrysts in olivine-phyric basalt and andesitic basalt (SiO2 <57wt%) are 0.5–2 mm in size, showing multiple generations, accompanied by elongate (less than 1.5 mm) plagioclases (Pl) (<7 in vol.%) in an Ol-bearing matrix. Typical andesites (SiO2 from 57 to 63wt%) show orthopyroxene phenocrysts (up to 1 by 2 mm) and tabular plagioclase (ca. 1 by 2 mm), up to 15 vol.% in some cases, accompanied by small amounts of mica and hornblende (NAG02008). The dacites (SiO2 <70wt%) and rhyolites (SiO2 >70wt%) typically show phenocrysts of albitic plagioclase, quartz, and subsidiary mica, aphyric exceptions recorded from Koshi-dake (SiO2 >76wt%). The basalt samples show compositional differences both within and between each volcanic center (Table 1). For example, SiO2 contents range between 48 and 53 (wt%) and Mg-numbers ((100 × (Mg/(Fe2+ + Mg)) between 47 and 63 in the 2.6 Ma in the Koshi-dake basalts, while the 5 Ma Kuro-dake basalts show SiO2 contents of 52–54 (wt%) and corresponding Mg-numbers of 51–47. Basalts from both areas are distinct from the 6.8 Ma Jinroku-yama basalts which show SiO2 contents between 50.5 and 56 (wt%) and corresponding Mg-numbers of 44–60 (Table 1). However, representative basalt compositions from Arita resemble those of the low-SiO2 Koshidake basalt (Table 1). Overall, the mentioned differences are likely to indicate one or more of the following effects: source heterogeneity24 and/or varying partial melting parameters — including pressure, temperature, and PH2 O .25,26 Plots of SiO2 vs K2 O in Fig. 3(a) illustrate the major element distinctions of basalts and andesitic basalts with K2 O contents <1.5 (wt%) that occupy a broad region within the so-called “medium-K” field.22,27 The voluminous Arita andesites show similar major element variation, Mg-numbers ranging from 37 to 50 (MgO = 2.3 and 3.4, and FeO* = 6.7 and 6wt%, respectively) although TiO2 contents (0.9–1.4wt%) and K2 O (2.3–3.7wt%), are somewhat higher, conforming to the high-K field (Fig. 3(a)). This is also the case for the dacites and rhyolites, for example, in Koshi-dake (SiO2 ranging 67–>76wt%, rhyolite) and K2 O, 2.5–4.5 (wt%) aside from calc-alkaline magmas of medium-K affinity, such as those from SW Japan5,28 (Table 1, Fig. 3(a)). In contrast, much of the Jinroku-yama basalts and Arita andesites fall in the calc-alkaline field as defined by the plots of FeO*/MgO versus SiO2 wt%23 (Fig. 3(b)), and almost certainly reflect source heterogeneity.

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Fig. 3. Plots of SiO2 vs K2 O (a) with boundaries22 separating low-K, med-K, and high-K (a), and plots of SiO2 vs FeO*/MgO (b) with fields of tholeiite and calc-alkaline after Miyashiro.23 Representative southwest Japan subduction-related Daisen samples are from Tamura et al.5 Note that much of the Jinroku-yama basalt and Arita–Imari andesite are plotted in the calc-alkaline field.

3.2. Trace element characteristics The combined variation of major and trace elements among the volcanic series, as exemplified by the positive covariance of Rb and Th versus SiO2 over a wide range of contents, are consistent with fractional crystallization effects, with or without crustal assimilation and/or mixing (AFC) processes (Table 1, Figs. 4(a) and 4(b)). However, plots of SiO2 against Ba (Fig. 4(e)),

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Fig. 4. Plots of SiO2 (wt%) against Rb, Th, Sr, Nb, and Ba (ppm). Generally positive correlation between SiO2 and Rb, Th and negative covariance with Sr might reflect fractional crystallization relationship among the volcanic rocks. Very low contents of Ba in the Koshi-dake rhyolite and various contents of Nb, in contrast, suggest source heterogeneity. (See details in text.)

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as for lavas with low Ba contents such as the Koshi-dake rhyolite (220– 360 ppm), cf. with those of the Arita rhyolite (500–700 ppm), cannot be explained in terms of AFC effects related to the accompanying basalt (350 ppm). Similarly, the negative correlation observed between SiO2 and Nb in the Koshi-dake basalts and rhyolites (Fig. 4(d)) also precludes an intracrustal genetic relationship. The contrasting Nb contents in these groups (<20 ppm cf. >40 ppm), for equivalent SiO2 contents, in the Kurodake basalts and andesites (Fig. 4(d)), appear to support this conclusion, especially given that fractionation of a Nb-rich phase is not expected during intravolcanic fractionation.29,30 The negative covariance from basalt to rhyolite observed in Fig. 4(d) may nonetheless indicate the possible involvement of Nb-depleted crustal material.6,31 Chondrite- and primitive mantle-normalized patterns of incompatible trace element (incl. REE) patterns for the basalts are typical of OIB (Figs. 5, 6).32,33 However, slight distinctions may be observed for each locality, especially in terms of high field strength elements (HFSE) such as Nb, Ta, and Ti (Figs. 5(a), 6(a)). Significant negative Eu anomalies observed in the Koshi-dake and some Arita rhyolites clearly reflect substantial plagioclase feldspar fractionation (Figs. 6(b), 7(b)). Compared to the Koshi-dake rhyolites, those from Arita show slightly more relative LREE enrichments and depletions in HREE and HREE. This observation again suggests that rhyolites from the two locations may tap distinct mantle source regions (Figs. 5(b), 6(b)). Rare earth element patterns in the Arita rhyolites, however, closely resemble the accompanying andesites (Figs. 5(c), 6(c)), suggesting that they may have genetic relationship although they are distinct from their accompanying basalts.

3.3. Sr–Nd–Pb isotopic compositions The basalt isotopic compositions, 87 Sr/86 Sr ranging 0.704–0.7048 and Nd/144 Nd ranging 0.5128–0.51268 (εNd = 2.5−0.9), are within the known range of Northern Kyushu Cenozoic intraplate basalts (Table 1, Fig. 7(a)).9,11 The rhyolites and andesites are the most enriched, with 87 Sr/86 Sr >0.7045–0.7052, corresponding to 143 Nd/144 Nd from 0.51269 to 0.51267 (εNd = 1−0.55) (Table 1). It is noteworthy that while the strontium isotopic ratios are high and variable in the rhyolites, their corresponding 143 Nd/144 Nd ratios vary within a much smaller range (0.5128–0.51267) (εNd = 2.75−0.7) (Table 1). Overall, the Sr and Nd isotopic compositions, 143

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Fig. 5. Chondrite-normalized rare earth elements32 showing (a) different patterns for basalts from different locations, at least two chemically different rhyolite types, (b) similarity in distribution configuration between Arita andesite and rhyolite, (c) An Iwate andesite (NE Japan Quaternary island arc lava)16 is shown for reference.

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Fig. 6. Primitive mantle-normalized trace element33 showing chemically different basalts (a), two rhyolite rock types with no genetic relation to basalt (b), and distribution pattern similarity between Arita rhyolite and andesite (c). Also shown is a 18 Ma tholeiite from Oki–Dogo island influenced by subduction-derived hydrous phases34 for reference. (See details in text.)

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Fig. 7. Plots of 87 Sr/86 Sr vs εNd (a) and 207 Pb/204 Pb vs 206 Pb/204 Pb (b) showing samples trending between depleted (DM) and enriched mantle (EM2) (and CC: continental crust). Some basalts plot toward enriched mantel type 1 (EM1) field similar to many Northern Kyushu Cenozoic intraplate basalts (crossed, data from Refs. 9, 11, and 17). DM, EM1, and EM2 are after Zindler and Hart.37 Symbols are as of Fig. 4.

including the most enriched rhyolite, are nonetheless within the range of Kyushu intraplate basalts.9,11,35 The Pb isotopic data (Fig. 7(b)) may be divided into two groups. Basalts from Arita, Koshi-dake, and a sample from Jinroku-yama, having low 206 Pb/204 Pb (<18.0) and 208 Pb/204 Pb (<38.45) plot in an EM1-rich field (equivalent to the “depleted” quadrant in the Sr–Nd diagram) similar to the OIB-like northern Kyushu and Japan Sea basalts.11,36 The second

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group comprises andesites and rhyolites, and some basalts from Jinrokuyama and Kuro-dake with 206 Pb/204 Pb >18.2 and 208 Pb/204 Pb >38.5, plotting toward EM2-rich field. These discrete Pb isotopic fields probably, at least in part, reflect distinct source regions.

4. Discussion 4.1. Origin of the isotopic enrichment Crustal contamination — whether by interaction between mantle-derived basaltic melt with wallrock via assimilation–fractional crystallization (AFC) processes or of their source regions, e.g., interaction with subductionderived fluids or melts — is a potential factor contributing to negative HFSE anomalies (Fig. 6) and overall high 87 Sr/86 Sr ratios in the Arita– Imari magmas (Table 1, Fig. 7(a)). However, the isotopic compositions, including those of the most enriched andesites and rhyolites, are within the known limit of northern Kyushu Cenozoic intraplate, mantle-derived basalts.7,11,38,39 For example, a Kuro-dake basalt (Table 1: NAG02026), showing typical OIB-like trace element patterns (Figs. 5 and 6), has an even higher 87 Sr/86 Sr ratio (0.70487) than many other andesites and rhyolites (Table 1, Fig. 7(a)). On the other hand, three basaltic samples from the Jinroku-yama area, which are among the most Sr–Nd isotopically depleted (Table 1, Fig. 7(a)), show relatively high 206 Pb/204 Pb and 208 Pb/204 Pb and low HFSE as compared to the basalts (Figs. 6(a) and 7(b)). Besides indicating the possible assimilation of HFSE-depleted crustal of material at shallow levels, their geochemical and isotopic signatures may also reflect source mantle characteristics.

4.2. Effects of assimilation–fractional crystallization While some of the intermediate to acidic lavas may be derivatives of parental mafic magmas via one or several intracrustal fractionation processes, the mantle-derived basalts described above are unlikely to be parental to the calcalkaline andesites, dacites, or rhyolites, given the significant differences in their incompatible trace element (Figs. 4–6) and isotopic compositions (Figs. 7(a) and 7(b)). While crustal-like involvement in the magmatic evolution is clearly consistent with a strong positive covariance between SiO2 and 87 Sr/86 Sr (Fig. 8),2,40,41 the discrepancy in abundance levels of Ba in the Koshi-dake

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Fig. 8. Plots of SiO2 vs 87 Sr/86 Sr shown with hypothetical binary mixing between a depleted source (87 Sr/86 Sr = 0.703, Sr = 40 ppm, SiO2 = 47wt%) and a SW Japan basement sediment (87 Sr/86 Sr = 0.7144, Sr = 85 ppm, SiO2 = 70wt%)35 to explain the chemical evolution of the volcanic series. The results, however, do not match with those of trace elements. (See details in text.)

basalt (380 ppm) and rhyolite (as low as 40 ppm) or Nb in the Kuro-dake basalt and andesite (referred to above) (Fig. 4) appear to preclude AFC processes as a dominant factor in explaining the compositional variation in the Arita–Imari volcanic rocks. Given that experimental data indicate that dacites and rhyolites can be generated by partial melting of, or equilibration with, hydrated basalt and/or andesite at pressures between ca. 7 and 2 kbar,42,43 they are clearly potential products of a combination of fractional crystallization, magma mixing, and assimilation and/or partial melting of lower or upper crust.3,43−47 Accordingly, Tamura and Tatsumi4 have proposed that dacites and rhyolites are most likely to have formed by partial melting of basalt and andesite in subduction-related settings. However, it is difficult to distinguish the geochemical and (particularly) isotopic systematics associated with partial melting models for rhyolite genesis from those generated simply by fractional crystallization given that both processes are likely to produce similar effects.

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The FARM process (fractional crystallization, assimilation, replenishment, and mixing) proposed by Baker et al.44 was applied to explain rhyolite formation in northern California43 and Unzen volcano, NW Kyushu.9 The results of a simple mixing calculation between compositions of a relatively depleted mantle source (87 Sr/86 Sr = 0.703, Sr = 40 ppm, SiO2 = 47wt%) and an enriched (crust-derived) granitic sediment from northern Kyushu (87 Sr/86 Sr = 0.714, Sr = 85 ppm, SiO2 = 71wt%)5,35 showed that mixing of 7%–10% sediment could explain the complete Sr isotopic range of andesite, dacite, and rhyolite from the Arita–Imari area (Fig. 8). However, the trace element mixing between the most primitive Arita basalt (NAG02001) and the northern Kyushu sediment, respectively showing 30 and 10 ppm Nb, and 10 ppm Th, 360 and 500 ppm Ba is not consistent with the isotopic mixing model. For example, adding 10% sediment with the basalt would produce Nb/Th ratios of 7.5–10 and Ba/Nb ratios of about 12.5 cf. 1.5–3.5 and 15–38 for Nb/Th and Ba/Nb, respectively (Table 1). Because each of these elements has closely similar melt-solid distribution coefficients48,49 (esp. Nb and Th) whose elemental ratios are essentially unaffected by fractional crystallization, magmatic AFC processes alone are not responsible for the variation in magmatic isotopic variation in this area.

4.3. Source heterogeneity The combination of relatively high 87 Sr/86 Sr isotope ratios and negative mantle-normalized Nb and Ta anomalies (Figs. 6–8) has been explained by several diverse mechanisms, including: (1) partial melting of or assimilation by lithospheric mantle and/or crust,2,40,42−44 (2) contamination of the magmatic source by subducting slab-derived hydrous fluids and/or melts,22,34,50 and (3) the presence of relict HFSE-rich phases during magma genesis and/or slab-derived fluid segregation.51−53 As a basis for evaluating potential geodynamic factors affecting mantle variation beneath the Arita–Imari area, the role of the underlying Pacific and Philippine Sea (PPS) — both subduting beneath the Japanese archipelago — plates should be evaluated. The volcanic front of the PPS is delineated by Aso Volcano28 in south central Kyushu (Fig. 1(a)) and Mts. Daisen and Sambe in southwest Honshu.50,54 The initial rifting phase of the Japan Sea (ca. 25–17 Ma; Ref. 55) was accompanied by low alkaline tholeiite (LAT) volcanism activity (e.g.) in the Oki–Dogo island area, Shimane region, in the Japan Sea, characterized by unusually high LILE and relatively low HFSE contents.34 These features

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have been interpreted to present partial melts of shallow lithospheric mantle characterized by longstanding enrichment34 in hydrous, subduction-related, metasomatic minerals.56 Middle Miocene (ca. 11 Ma) LAT and evolved calcalkaline (andesite and rhyolite) eruptive products occur in the nearby Matsue Formation showing transitional arc — intraplate trace element and isotopic characteristics, viewed as products of depleted mantle, modified by slab-derived fluid,51 because the leading edge of the PPS plate was not beneath the area during the Miocene50,54,57,58 attributed subductionrelated volcanism in the Shimane area to Pacific plate subduction. Because the late Miocene Arita–Imari activity, located more than 100 km to the west of the volcanic front, is not directly related to PPS subduction,57,60 it is unlikely that the OIB-like basalts are parental to the andesite–dacite–rhyolite (Figs. 4–6), the latter being more plausibly ascribed to a distinct subduction-related (calcalkaline) tholeiitic parent.22,60 This implies the likelihood of coexisting OIB-like and subduction-related mantle sources beneath the Arita–Imari, analogous to that reported elsewhere.61−63 The presence of primitive OIB-like mantle, albeit a subsidiary component, beneath such volcanic arcs as those in Northeast Japan Arc64,65 and the Aleutians66 suggests coexisting with MORB-like mantle modified by subduction-derived metasomatic contaminants.67,68 Thus, in summary, calcalkaline volcanism in the Arita–Imari area, and elsewhere is SW Japan, is considered to result from the melting of the subcontinental lithosphere mantle affected by Pacific slab-derived contamination since before the Japanese archipelago separated from Eurasia.34,50,54,69

5. Summary Late Neogene magmatism in the Arita–Imari area, northern Kyushu, SW Japan comprising basalt–andesite–dacite and rhyolite, accompanied lithospheric extension during plate kinematic readjustments following the opening of the Japan Sea. The basalts show OIB-like geochemical affinity, their isotopic compositions suggesting a range EM1-like asthenospheric sources and with superimposed effects of EM2-like lithospheric mantle or crustal components; the relatively high LILE/HFSE ratios in the latter, suggest either the presence of crustal material in lithospheric source regions or its assimilation during magmatic ascent and emplacement. Both of the EM isotopic endmembers show a strong spatial dependence in the basalts, suggesting

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regional source heterogeneity in the lithospheric mantle in northern Kyushu to be a significant factor.11,17 The (calcalkaline) andesite and dacite–rhyolite series show a range of similar incompatible element distributions, distinct from OIB-like patterns, but consistent with differentiation from mafic parent melts. It is unlikely that these magmas are products of the OIB-like basalts and more likely derived from a distinct calcalkaline tholeiitic magma. Derivative calcalkaline magmas probably evolved via a process combining the effects of fractional crystallization, assimilation, replenishment, and mixing (FARM).44 The composition of the assimilant involved is consistent with the regional crustal granitic sediments whose geochemical character contrasts strongly with wallrock of transitional crustal magma chambers. The chemical and isotopic diversification thus depends largely on the extent of wallrock melting and interaction with mantle-derived mafic magmas and their differentiation products. The occurrence of OIB-type basalts and calcalkaline-type andesites suggests that the two mantle sources may coexist beneath the Arita– Imari area and elsewhere in Southwest Japan — the first from fertile asthenospheric reservoirs within the mantle wedge, the second generated from the mantle metasomatized by (Pacific plate) subduction-dating before the separation of the Japanese archipelago from Eurasia.

Acknowledgments The manuscript was benefited from intensive discussions with Isoji Miyagi (GSJ) and Martin Flower (UIC). Osamu Ishizuka and Naoko Honda are gratefully acknowledged for assistance in ICP-MS analysis. The authors thank an anonymous reviewer for helpful reviews. Kenji Satake is thanked for editorial handling of the manuscript. This research project has been conducted under the research contract with the Japan Nuclear Energy Safety Organization (JNES).

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4. Y. Tamura and Y. Tatsumi, J. Petrol. 43 (2002) 1029–1047. 5. Y. Tamura, M. Yuhara, T. Ishii, N. Irino and H. Shukuno, J. Petrol. 44 (2003) 2243–2260. 6. S. R. Taylor and S. M. McLennan, Phil. Trans. Royal Soc. London 301 (1981) 381–399. 7. H. Kurasawa, Geological Survey of Japan, Report 217, Kawasaki, Japan, (1967). 8. K. Uto and Y. Tatsumi, The Island Arc 5 (1996) 250–261. 9. N. Hoang and K. Uto, Chem. Geol. 198 (2003) 249–268. 10. H. Mashima, J. Volcanol. Geotherm. Res. 131 (2004) 333–349. 11. K. Uto, N. Hoang and K. Matsui, Tectonophysics 393 (2004) 281–299. 12. T. Seno, The Island Arc 8 (1999) 66–79. 13. S. Imai, K. Sawamura and T. Yoshida, Geology of the Imari district, Geological Sheet Map of 1:50,000, Geological Survey of Japan (1958) (in Japanese with English abstract). 14. T. Yanagi and S. Maeda, Phys. Earth Planet. Interiors 107 (1998) 203–219. 15. K. Uto, O. Ishizuka, A. Matsumoto, H. Kamioka and S. Togashi, Bull. Geol. Surv. Japan 48 (1997) 23–46. 16. J. Itoh, K. Uto, A. Matsumoto, M. Sudo and N. Hoang, Japan Earth Planet. Sci. Meeting g017 (2004) 004. 17. N. Hoang and K. Uto, Earth Planet. Sci. Lett. 249 (2006) 229–240. 18. O. Ishizuka, R. N. Taylor, J. A. Milton and R. W. Nesbitt, Earth Planet. Sci. Lett. 211 (2003) 221–236. 19. C. Deniel and C. Pin, Analytica Chimica Acta 426 (2001) 95–103. 20. C. Pin and J. F. Santos Zalduegui, Anal. Chimica Acta 339 (1997) 79–89. 21. N. Hoang, K. Uto, H. Hoshizumi and K. Oguri, IUGG Meeting A-541 (2003) Sapporo. 22. J. B. Gill, Orogenic Andesite and Plate Tectonics (Springer-Verlag, New York, 1981). 23. A. Miyashiro, Am. J. Sci. 274 (1974) 321–335. 24. S. Turner and C. Hawkesworth, Chem. Geol. 120 (1995) 295–314. 25. K. Hirose and I. Kushiro, Earth Planet. Sci. Lett. 114 (1993) 477–489. 26. I. Kushiro, in Earth Processes: Reading the Isotopic Code, eds. A. Basu and S. R. Hart, Vol. 95 (American Geophysical Union, 1996), pp. 109–122. 27. R. J. Arculus, J. Petrol. 44 (2003) 929–935. 28. T. Nakada and H. Kamata, Bull. Volcanol. 53 (1991) 182–194. 29. D. Ionov and A. W. Hofmann, Earth Planet. Sci. Lett. 131 (1995) 341–356. 30. B. S. Kamber, A. Greig, R. Schoenberg and K. D. Collerson, PreCambriar Res. 126 (2003) 289–308. 31. D. Ben Othman, W. M. White and J. Patchett, Earth Planet. Sci. Lett. 94 (1989) 1–21. 32. N. Nakamura, Geochim. Cosmochim. Acta 38 (1974) 757–775. 33. W. F. McDonough, S.-S Sun, A. E. Ringwood, E. Jagoutz and A. W. Hofmann, Geochim. Cosmochim. Acta 56 (1992) 1001–1012. 34. K. Uto, E. Takahashi, E. Nakamura and I. Kaneoka, Geochem. Journal 28 (1994) 431–449.

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65. T. Sano, T. Hasenaka, A. Shimaoka, C. Yonezawa and T. Fukuoka, Earth Planet. Sci. Lett. 186 (2001) 187–198. 66. J. D. Morris and S. R. Hart, Geochim. Cosmochim. Acta 47 (1983) 2015–2030. 67. M. J. Hole, A. D. Saunders, G. Rogers and M. A. Sykes, Geol. Soc. Spec. Publ. 81 (1995) 265–285. 68. M. Gorring, B. Singer, J. Gowers and S. M. Kay, Chem. Geol. 193 (2003) 215–235. 69. Y. Miyake, Geochem. J. 28 (1994) 451–472.

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Advances in Geosciences Vol. 13: Solid Earth (2007) Ed. Kenji Satake c World Scientific Publishing Company


LANDSLIDE HAZARD ZONING OF THE MURAVERA HILLSIDE (SARDINIA, ITALY) GIULIO BARBIERI∗ and PAOLO CAMBULI Department of Territorial Engineering, University of Cagliari, Piazza d’Armi Cagliari, 09123, Italy ∗ [email protected]

Here a method for zoning landslide hazard is described adapting at slope scale the method proposed by the Italian Geological Service for creating the 1:50.000 in scale geological risk map related to slope instability. The approach consists of a preliminary zoning for roughly dividing the study area up into a discrete number of smaller areas, sufficiently homogeneous as to their natural susceptibility to gravitational instability. This zoning takes into account lithological features, slope and plant coverage, all factors that have a direct influence on slope stability conditions, and are in the meantime easy to be measured and to be mapped. The landslide hazard zoning is obtained by assigning a numerical weight to each factor, in relation to the degree of instability determined therefrom and then summing for each area all the numerical weights produced by the factors examined. This procedure has been performed in GIS environment as it provides numerous calculation procedures for converting the vector data for the basic mapping information into raster data and their subsequent representation in finite elements using a numerical model. The proposed method has been applied to the built-up hillside of Muravera, in southeastern Sardinia, at the mouth of Flumendosa River.

1. Introduction The interventions planning aimed at mitigating the landslide risk in a certain area demands a preliminary evaluation of the landslide hazard, that, once all the elements potentially at risk have been identified and their vulnerability evaluated, allows to define the level of risk of the areas located in the territory and consequently to establish a scale of priority between the stabilization works relying on the different levels of risk that the areas present. The evaluation of the landslide hazard, intended with Varnes1 as the probability that a certain landslide phenomenon may happen in a certain area and in a certain timecourse, is recognized as a very complex process, because the conditions of the slope stability depend on several factors as 57

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lithological, morphological, hydrological, geotechnical, hydrogeological, and those concerning the land use. Though, the need for the hazard evaluations on regional scale make the adoption of geotechnical quantitative impractical methods, because a physical and mechanical characterization of all the rocks involved in the stability evaluation of wide areas is impossible. Therefore, qualitative or semi-quantitative methods are usually employed, such as the Overlay Mapping,2 that produces an evaluation of the landslide hazard through a weighted overlapping of all the different factors that affect the slopes stability. In order to be feasible, this method takes into consideration those factors of landslide susceptibility that are already available and mapped at the regional scale such as lithology outcrops, hillside slope, and land use. While conferring variable numerical weights according to their real influence on the slope stability condition to the latter parameters (hillside slope and land use) is quite easy, the assignment to the lithological factor of a weight that truly corresponds to the real stability conditions is indeed arduous and problematic, because such conditions are not exclusively bound to the lithological characteristics of the rock formations (granite, basalt, clay, etc.), but depend on the physical and mechanical conditions of the formation (degree of weathering, joints frequency, compactness). In this work, a practical method for the correct evaluation of the lithological factor weights is described, exemplifying this process through the landslide hazard zoning of the county territory of Muravera, in the southeastern part of Sardinia (Italy).

2. Geological and Geomorphological Setting of the Muravera Hillside The built-up area of Muravera extends on the right bank of the Flumendosa river, a few kilometers from the Mediterranean sea, at the foot of a large hillside of Paleozoic metamorphites, that dominate and impend over it. The most ancient formation, the San Vito Sandstone, is composed of a thick, weakly metamorphosed sequence of sandstone, quartzites, and shales. These are overlain by metavolcanites, known as the Serrabus Grey and White Porphyrites. The volcanic formations are followed by metasandstones and metaconglomerates, the Punta Serpedd`ı Formation, by silicified limestones, the Tuviois Formation and last by metasediments of Silurian–Devonian and likely Carboniferous age, the Serra S’Illixi Group. The foothills are composed entirely of Quaternary clastic sediments, like talus and ancient and recent alluvium.

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The poor plant coverages and the anthropic interventions on the area make it exposed to possible phenomena of debris flows, that may be activated by particularly rough meteorological events. Phenomena of falls and topples by detachment and rolling of etherometric blocks from the rocky subvertical slopes that rise in the upper part of the site are also possible. 3. Landslide Hazard Assessment Conforming to the specifications included in the Guidelines for mapping hydraulic and geomorphologic risk in the Sardinian Region,3 the analysis of landslide hazard has been carried out by evaluating and pondering, in every site potentially exposed to risk, the influence that the different factors of landslide susceptibility (geological and technical factors, morphological, vegetational) have on the stability conditions of the slopes. Thus, for every factor of landslide susceptibility, a specific thematic map has been drawn: the landslide susceptibility factors have been indexed through the assignation of numerical weights proportional to the supposed incidence of the single factor on the possible development of landslide phenomena. Careful consideration of the single factors has been made by adopting the same range of weights provided for every factor in the abovementioned Guidelines. The map of landslide hazard has been obtained through the overlapping and the sum of all the weights assigned to all the landslide factors considered. The evaluation of the hazard has been at first conducted on the basis of three fundamental factors of the landslide susceptibility: • the hillside slope, • the land use, • the lithology of outcropping formations.

3.1. Basic cartography In order to be able to analyze and weigh these factors, it has been necessary to survey and to adapt to the scale of detail chosen, 1:2000, the following basic cartography: • Map of the hillside slope, built dividing the area into 25×25 m meshes and employing the numerical 3D map on 1:2000 scale of the county territory of Muravera.

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• Map of the land use, built upon the Map of the Land use of the Sardinia Region, 2003 Edition, on 1:25000 scale, verified and corrected through direct surveys in the countryside. • Geological map, drawn according to the Geological Map F.549, Muravera Section II, on 1:25000 scale, of the Sardinia Region, verified and corrected through direct surveys in the field (Fig. 1).

Fig. 1.

Geological map of the Muravera hillside.

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3.2. Weighted cartography Using the basic cartography, the weighted maps of the landslide susceptibility factors have been built. Weighted map of the hillside slope, takes in account the intensity of the gravitational destabilizing component. The weighted map of the hillside slope has been built from the corresponding basic map, using the same weights specified in the aforementioned Guidelines (Table 1). Weighted map of the land use, taking into consideration an active, external, and time-variant parameter, gives a measure of the impact of the plant cover and of the anthropic activity on the conditions of hillside stability. The weighted map has been derived from the correspondent basic map, using the same weights and land use classes specified in the aforementioned Guidelines (Table 2). Weighted map of lithology, aims to represent, through the peculiar characteristics of the lithological formations, the extent of the resistant forces that are opposed to the potential landslide phenomena. Lithology is a complex factor, mainly evaluated from a geotechnical point of view, that Table 1. Slope classes α

Slope classes and relevant weights.

0%–10%

11%–20%

21%–35%

36%–50%

>50%

+2

+1

0

−1

−2

Weights

Table 2.

Land use classes and relevant weights.

Land use classes Residential areas, entertainment, and sport areas Commercial and industrial/handicraft establishments Build sites, sowable lands Artificial grasslands Orchards Woods Natural grazing lands Mediterranean scrub Canals and waterways Bared rocks

Weights 0 0 −1 −2 0 +2 0 +1 −2 −2

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takes into account all the geological, structural, physical, and mechanical characteristics, in relation to the potential landslide phenomenon. In order to weigh correctly the landslide susceptibility of the rock formations described in the Geological Map, the following parameters, that altogether determine the geomechanic behavior of every single lithology, have been evaluated: for rock formations: • • • • •

weathering degree depth of the weathering spacing of joints attitude of joints compressive strength of the sound rock

for loose formations: • • • •

degree of cementation thickness of the scree deposit compactness permeability

These parameters have been carefully evaluated through direct on-site surveys and measurements taken in each point of a dense survey network, constituted by 117 stations distributed almost evenly on all the studied area (Fig. 2), in order to determine the weights of the single parameters and consequently, through the sum of the weights, the global weight of the lithological factor. The assessment of the parameters and the division into classes have been made following the specifications included in the Guidelines of the Italian Geological Service for carrying out a geologic hazard cartography.4 All the parameters considered have been divided into four classes, with an impedance degree to the landslide phenomenon decreasing from class I, the most stable, to class IV, the most unstable (Table 3). The total score given to the lithological factor in every survey station, calculated as the sum of the weights given to the single parameters taken for examination, varies between a minimum of 4 and a maximum of 20 for the rock formations and between a minimum of 4 and a maximum of 16 for the loose formations, and has been normalized in the range from 1 (minimum) to 9 (maximum) as the Guidelines for mapping hydraulic and geomorphologic risk in the Sardinian Region specific for the lithological factor.

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Weighted lithology map with network of survey stations. Fig. 2.

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Geomechanical parameter classes and relevant weights.

Geomechanical parameters Weathering degree Weathering deepness, z(m) Joints spacing, s(cm) Joints attitude Compressive strength, σ(kg/cm2 ) Cementation degree Scree thickness, d(m) Compactness degree Hydraulic conductivity, k(m/s) Weights

Class I

Class II

Class III

Class IV

None z<1

Moderate 1
High 3
Like soil z>5

s > 100 Horizontal σ > 40

30 < s < 100 Vertical 20 < σ < 40

5 < s < 30 Deep slope > α 10 < σ < 20

s<5 Deep slope < α σ < 10

High d<1 Very compact k > 10−3

Moderate 1
Light 3
None d>5 Loose k < 10−9

4

3

2

1

The indexed map of lithology has been realized for all the areas under study, distributing with geometrical criterion the exact values calculated in every survey station until the middle point between two contiguous stations and avoiding at the same time to cross the border of another lithology (Fig. 2).

3.3. Map of the landslide hazard by overlapping The overlapping of the weighted maps of hillside slopes (range −2 ÷ 2), land use (range −2 ÷ 2), and lithology (range 1 ÷ 9) produces at last a cartography of first synthesis of the landslide hazard in the considered area (range −3 ÷ 13) in which every elementary area of the territory under study is characterized by a numerical index of hazard, obtained by arithmetical sum of the factor weights, that translates in a numerical value the influence that these three factor have on the conditions of the hillside stability. This numerical index range has been divided into four classes of even extent, with different degrees of hazards, according to the scheme shown in Table 4. Table 4. Landslide hazard classes Intensity Weights h

Lanslide hazard classes.

Hg1 Moderate 9 < h < 13

Hg2 Medium 5
Hg3 High 1
Hg4 Very high −3 < h < 1

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Landslide hazard map by overlay of lithology, slope, and land use. Fig. 3.

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The influence of the lithological and land use factors may in some cases lead to classify flat territories as medium- or high-hazard areas, while these could be easily accounted as zones with low possibilities of landslide phenomena; so all the territories that present slopes lower than 20% have been automatically classified as class Hg1, without keeping in account their values in the lithology and land use (Fig. 3).

3.4. Map of the landslide hazard due to falls and topples The overlay mapping method, given that it performs an evaluation of the hazard on the orthogonal projection of the territory map, does not allow to evaluate the landslide hazard of subvertical rocky walls, typically affected by potential phenomena of falls and/or topples. To keep these factors also into account, the class of landslide hazard due to falls and topples has been evaluated by making in the subvertical rocky sites the same kind of surveys made for the evaluation of the lithological factor. Once the areas potentially affected by rock falls or topples had been identified, the extension of the area subject to landslides has been estimated by evaluating the possible paths and the limits of the landslides invasion, according to the typological characteristics of the gravitational movement and to the morphological and vegetational characteristics of the site under the rock face. In this way, for all the areas considered, a map of the landslide hazard caused by falls and topples has been drawn, divided into the same four classes of hazard (Fig. 4).

3.5. Map of the landslide hazard due to debris flows The hazard evaluation of debris flows has been carried out through the following phases: • surveying and mapping of spatial distribution of scree deposits (Fig. 1); • assessment, for every scree deposit, of the overcoming of the stability limit of Takahashi5 for given return periods: 50, 100, 200, and 500 years; • evaluation of the extension and propagation of debris flows, following lines of maximal slope, until the confluence in the channel; • evaluation of the progressive attenuation of the debris flow intensity, due to the reduction of the kinetic energy, caused by friction and by reduction of the hillside slope, with a consequent progressive granulometric segregation of the solid matrix.

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Landslide hazard map due to falls and topples. Fig. 4.

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Landslide hazard map due to debris flows. Fig. 5.

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Final landslide hazard map of Muravera hillside. Fig. 6.

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In this way, a map of the landslide hazard caused by debris flows has been drawn, divided into hazard classes according to the return period (Fig. 5).

3.6. Final map of landslide hazard The final map of landslide hazard has been obtained by overlapping the weighted map of lithology, hillside slope and land use, the map of the landslide hazard due to falls and topples, and the map of the landslide hazard due to debris flows (Fig. 6).

4. Conclusions In this study, the assessment of the landslide hazard has been mainly focused on the effort of evaluating the lithological factor with a weight value correctly proportional to the actual impact that the geomechanical behavior of the lithological formations has on the condition of slope stability. Therefore, the main physical and mechanical parameters (deepness and degree of the weathering, compactness, spacing and attitude of the joints, compressive strength, permeability) of the lithological formations have been evaluated through direct on-site surveys and measurements taken in each point of a dense survey network, and each parameter has been attributed a numerical weight proportional to the relative impact on the landslide hazard. The distinctive weight of every lithological formation has then been determined through the algebraic sum of the weight attributed to every single factor. The method proposed in this study demands only on-site investigations and semi-quantitative surveys in the countryside, and consequently it allows evaluations of hazards at regional scale, or for wide areas, in circumstances where the adoption of quantitative and purely geotechnical methods are impractical, because of the impossibility to proceed with the physical and mechanical characterization of all the rock formations involved in the evaluation of the stability of wide areas.

References 1. D. J. Varnes and IAEG Commission on Landslides, Landslide Hazard Zonation — A Review of Principles and Practice (UNESCO, Paris, 1984). 2. P. Aleotti and R. Chowdhury, Landslide hazard assessment: Summary review and perspectives, Bull. Eng. Geol. Environ. 58(1) (1999) 21–44.

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3. D. Dovera, M. Mancini and M. Salis, Attivit` a di individuazione e perimetrazione delle aree a rischio idraulico e geomorfologico e relative misure di salvaguardia. Linee guida (Regione Autonoma della Sardegna, 2000). 4. M. Amanti, A. Carrara, G. Castaldo, P. Cosimo, G. Gisotti, M. Govi, G. Marchionna, R. Nardi, M. Panizza, M. Pecci and G. Vinello, Linee guida per la realizzazione di una cartografia della pericolosit` a geologica connessa ai fenomeni di instabilit` a dei versanti alla scala 1:50.000 (Presidenza del Consiglio dei Ministri–Servizio Geologico d’Italia, 1992). 5. T. Takahashi, Debris flow, IAHR Monograph (Balkema, Rotterdam, 1991).

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Advances in Geosciences Vol. 13: Solid Earth (2007) Ed. Kenji Satake c World Scientific Publishing Company


AN INTEGRATIVE GEOSCIENCE DATA MODEL BY LINKING FIELD-SPECIFIC DATA MODELS IN DIGITAL GEOLOGIC MAP, EARTH RESOURCE, AND GEO-HAZARD LUCAS DONNY SETIJADJI∗,†,‡ and KOICHIRO WATANABE∗ ∗Department of Earth Resources Engineering Kyushu University, 744 Motooka, Nishi-ku Fukuoka, 819-0395, Japan †Department of Geological Engineering Gadjah Mada University, Grafika 2, Bulaksumur Yogyakarta, 55281, Indonesia ‡ [email protected]

There are now several object-relational geoscience data models available, such as the digital geologic map and mineral exploration models. This study pursues an integrative model by integrating them and extending their contents with data from our research projects in Japan and SE Asia. Current result is the first version of GeoSEA data model that can support different kinds of geoscience projects in geological mapping, earth resources, and geo-hazards at a continental margin setting such as SE Asia.

1. Introduction The development of object-relational GIS in early 2000s1,2 makes possible the integration of GIS and object-relational databases into a customized GIS for specific applications, including geoscience. A properly designed database using object-relational technology enables modeling specific phenomena inside a database while it still supports an easy use of data analysis.1 Using this basic concept, several database model projects in geoscience have evolved, such as the North America Digital Geologic Map Data Model (NADM), the eXploration and Mining Markup Language (XMML), the Commission for the Management and Application of Geoscience Information (CGI) of the International Union of Geological Sciences (IUGS), and the ESRI Geology Data Model (EGDM). 73

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In this chapter we will describe our database modeling project for the purpose of studying geology of continental margin such as Japan and SE Asia. As the geology of continental margin is a complex interplay of different geological processes, and as we are also interested in contributing to many research fields, our aim is to develop an integrative geosciences database model that can serve many geologic applications. The basic approach is by linking several existing database models, extending their current contents, and implementing results using a selected physical platform.

2. Geoscience Data Model Projects 2.1. Major geoscience data model projects Started almost simultaneously around year 2000, several international data model projects are now evolving in different fields. Today, these initiatives might have been culminating at the IUGS-CGI data model project (https://www.seegrid.csiro.au/twiki/bin/view/CGIModel). The aim of CGI is to enable the global exchange of knowledge about geoscience information and systems. Its current product is the GeoSciML data model, that draws from many geoscience data model efforts, and from these establishes a common suite of feature types based on geological criteria (units, structures, fossils) or artefacts of geological investigations (specimens, sections, measurements). A number of predecessor projects have a strong influence on the development of GeoSciML. These include activities by national geological surveys such as British, Japan, Canada, and the United States. The latest two countries have established the North American Data Model for geological maps project (NADM; http://geology.usgs.gov/dm/) that in the case of the United States has evolved into the National Geologic Map Database project (NGMDB; http://ngmdb.usgs.gov). This project focuses on developing standards on description, classification, and interpretation of geological features in digital geologic-map databases. The conceptual design was conceptualized as the NADM-C1.0 data model.4 From the minerals industry sector, the XMML data model project (https://www.seegrid.csiro.au/twiki/bin/view/Xmml) works for mineral industry in order to support exchange of mineral exploration information. The ESRI’s EGDM project (http://support.esri.com/datamodels) focuses on implementation of the NADM model using the ESRI ArcGIS software. This project has produced the EGDM 812 data model,3 the ArcGIS implementation of the NGMDB Geologic Map Feature Class model,5 and

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the ArcGeology v.1.9 The Open Geospatial Consortium (OGC) has also been working on this initiative, and one important product is the OGC 05-087r4 on Observations and Measurements.8 All of these products, except for the XMML, are focusing on digital geological map. In other words, there are still many fields unfinished or even untouched. 2.2. About this project This project was initiated in 2003 when the senior author was in an internship program with ESRI at Redlands, California. With an initial support of ArcGIS software from ESRI, this data model project was started as part of the senior author’s doctoral thesis at Kyushu University, Japan, with the focus of modeling on island arc magmatism and earth resources.6,7 This initiative was then disseminated into the Asian University Network (AUN) program in the field of geological engineering, and the scope was enlarged into other geoscience fields such as geo-hazards. Since the year 2006, this data model project is financially supported by a Grant-in-Aid for Scientific Research from the Ministry of Education and Science, Japan. As the scale of this project is much smaller than other major projects mentioned above, at the core we merely take advantages from the development of other data model projects. Then, at parts that are not yet touched, we extend the model by self-modeling the data that are directly related with our research activities. Also, this project pursues an integrated model, in which one core geoscience data model can be used to serve different field-specific projects. This is done through linking the core geological map data model with other task-specific data models for earth resources and geo-hazards. 2.3. Case study Our research group is very active in conducting research studies on mineral resources in Japan and many Asian countries. However, for the case study on an integrative data model, we select the Yogyakarta region in central Java island, Indonesia. There are several reasons for this selection. First, this city is the center of geoscience education in Indonesia. Second is the existence of various geoscience research projects that we directly work here, such as the study on still very active Merapi volcano, the devastated earthquake 27 May 2006, the porphyry deposit prospect at Selogiri district, a small geothermal prospect at Parangtritis beach, and a major Dieng geothermal field.

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3. Proposed Integrative Data Model 3.1. Modeling approach and platform Our model uses the conceptual model of NADM-C1.0 as the base for developing extended data models for earth resources and geo-hazards. The reason is that the NADM-C1.0 works on basic geological information, i.e. concepts related to information presented on digital geologic maps. Furthermore, the conceptual design of NADM-C1.0 has also been accepted by other data models. Following the database design concepts by Longley et al.2 and Zeiler,1 we came through three stages of database design: conceptual, logical, and physical models. In the conceptual model, all collected datasets are organized in order to define all object classes. We then defined database objects and their relationships. A conceptual design was then drawn into a logical model using the computer-aided software engineering (CASE) tools using a unified modeling language (UML) diagram. The logical model was developed by matching the conceptual object types to specific platform used for the real implementation of databases. As we use the ESRI ArcGIS and Geodatabase platforms, several ArcGIS-proprietary standard classes were added for modeling spatial features and representation. Finally, physical database was implemented by populating the Geodatabase schema with real data. With directly doing research projects on geology, earth resources, and geo-hazards in Japan and SE Asian countries, we can now produce the first version of integrative data model, i.e. the GeoSEA Data Model 1.0. This version is developed from our previous data model on earth resources6 through adaptation with new developments from other data model projects and from our projects on the Yogyakarta earthquake and Merapi eruption 2006.

3.2. GeoSEA data model structure The structural organization of data model is organized into packages, supergroups, groups, and classes. Packages are containers used to organize UML elements into specific projects. This is merely an organization issue as UML elements of different packages can actually be shared with or moved into other packages. Within each package, UML elements are organized into supergroups, groups, and individual classes. In the current model, there are three packages created, i.e. the geologic map, earth resource, and geo-hazard

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Packages within the GeoSEA Data Model v.1.0.

models (Fig. 1). The geologic map package becomes a center of modeling as most object classes are modeled here. The earth resource and geo-hazard packages are used to organize UML elements which are very case-specific for respective purposes.

3.3. Detailed descriptions of object classes The contents of geologic map package as the center of data model are shown in Fig. 2. The top level object classes of geological map model consist of GeologicConcept, MetaData, and Feature classes that inherit from the ESRI Class: Object. The GeologicConcept is a supergroup class that compiles all geo-object names, which are then further grouped into several group classes. The EarthMaterial, GeologicProperty, GeologicStructure, GeologicUnit, GeologicProcess, GeologicRelation, and GeologicEvent group classes were adapted from the NADM-C1.0,4 while the EarthResource, GeologicSample, GeologicMeasurement, and GeologicVocabulary group classes were established on their own.6 The GeologicMeasurement has also been modeled at the OGC project together with the GeologicObservation.8 We insert the GeologicVocabulary group that compiles all geologic concept names using standard classification, such as from the NADM Geological Map Unit Classification Version 6.1 (http://geology.usgs.gov/dm/). Note that the EarthResource group class is part of the EarthMaterial group, while the Geo-hazardEvent is a member of the GeologicEvent group. The Feature is a spatial representation of data through a map and is a proprietary of the ESRI ArcGIS. At a more platform-free definition, the MappedFeature group class is often used in other projects (i.e. NGMDB, XMML, and CGI data models). We adapted the classification of MappedFeature based on the NGMDB model,5 and add it with SamplingFeature and DrillHoleSegmentFeature. Especially for the drillhole

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Top level object classes of Geologic Map package. Fig. 2.

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data, the reason for this addition is that we have developed a method to represent drillhole route as 3D line using linear referencing tool.6 The earth resource data model package (Fig. 3) is refinement of our previous model6 with additional classes derived from the XMML data model especially related with industrial activities on metallic deposits. These classes include the mineral deposit classification, commodity, quantity measures, mining activity, and their products. From our side, we have been working on two earth resource types, i.e. metallic deposits6 and geothermal.7 Both the earth resources are typically associated with the hydrothermal alteration zones (such as our case studies at Dieng geothermal fields and Selogiri porphyry prospect), so that we add a class related with hydrothermal alteration. The most intensive modeling so far done, which is also becoming the most significant contribution originated from this project is on modeling the GeologicMeasurement with a focus on analytical works during the studies of metallic deposits, either in the exploration or in the evaluation stages (Fig. 4). However, as many of these analytical works are also applicable in other geosciences fields, such as radiometric dating and structural measurement, we put this group class within the Geologic Map package rather than in the Earth Resource. The geo-hazard package contains classes as seen in Fig. 5. Geo-hazards are parts of geological events that occur during particular periods. Based on the real cases in our study area, there are four types of geo-hazards that have been modeled, i.e. earthquake (Yogyakarta earthquake 27 May 2006), volcanic eruption (Merapi volcano eruptions 2006), tsunami (17 July 2006 tsunami along the south Java coast), and landslide (many landslides events were driven by 27 May 2006 earthquake). We model interrelationships among different types of geo-hazards. For example, the 27 May 2006 Yogyakarta earthquake has triggered many landslides. There was also relationship between this earthquake and volcanic activity of Merapi. The 17 July 2006 tsunami was produced by a M 7.7 earthquake off the coast of Java. Finally, Merapi-style eruption is characterized by dome collapse that can be associated with landslide events. We model geo-hazard products into two kinds: earth materials (tephra, lahar, lava, debris deposit, and tsunamistone) and casualties. Earth material products can either be mapped for geological map or hazard map establishment. On the other hand, casualties are only used for generating geo-hazard map. In terms of geo-hazard mitigation, investigation on geo-hazard events can consist of observation and measurements (either

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Object classes within the Earth Resource package. Fig. 3.

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Object classes within the GeologicMeasurement group. Fig. 4.

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Object classes within the Geo-hazard package. Fig. 5.

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by taking samples or not). Most kinds of measurement methods have actually been modeled inside the geologic map package (Fig. 4), so that the geo-hazard package just makes a link to the related geological measurement classes.

4. Demonstration of Implementation The design model has been implemented using the ArcGIS 9.1 Geodatabase platform (MS Access format) in the Yogyakarta region, Indonesia. Figure 6

Fig. 6. Implementation of data model in the Yogyakarta region, central java, Indonesia using ArcGIS. The base map is a 3D view of digital geologic map of Yogyakarta region, while three overlying maps (Merapi, Selogiri, and Yogyakarta) represent three different research projects in volcanology, mineral exploration, and earthquake hazard that share the same core database model. An example of relational database structure linked to maps is also shown.

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demonstrates how four involving and on-going research projects can share one integrative GIS data model. These projects are: (1) digital geological map project of the Yogyakarta region; (2) earthquake hazard study related with Yogyakarta earthquake 27 May 2006; (3) volcanology study of Merapi eruption; and (4) mineral deposit study on porphyry Cu–Au prospect at Selogiri district. In order to serve these four different projects, we created one MS Access database for each project. Actually, we can create only one, big database for this purpose, especially if we use an “enterprise” database such as Oracle. However, to keep the uniqueness of each project and to maintain the performance of an MS Access database, we decide to make one database for each project. The concept of integrative database model here is more emphasized on its conceptual-design level rather than its physical implementation level. Each data model package of Geologic Map, Geo-hazard, and Earth Resources (Fig. 1) is then implemented into one or more physical databases. In this case, the Geologic Map package is implemented as the Yogyakarta Geologic Map database, the Geo-hazard package as the Yogyakarta Earthquake 2006 and the Merapi volcano databases, and the Earth Resources package as the Selogiri porphyry Cu– Au database. The majority of datasets are included in the Yogyakarta Geologic Map database, as it covers almost all geological concepts conceptually (Fig. 2) and many different projects can share these generic data. For example, a fault map is a part of Geologic Map database rather than Geo-hazard database as it is used for many different purposes. However, the event of earthquake on 27 May 2006, its aftershock events, and casualties map are included in the Yogyakarta Earthquake 2006 database. The same approach applies for Merapi and Selogiri projects. Figure 6 is a visualization of interaction among four GIS databases as described above. Each project has its own database, but they also share one generic database to be used in all projects, i.e. the geologic map.

5. Conclusions This data model project has successfully implemented an integrative approach to link application-specific data models (i.e. earth resource and geo-hazard) with the core digital geologic map data model. This method gives invaluable benefits for managing our projects as we can use one geospatial information system to support different geoscience

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applications and to synergize contributions from each geoscience field for more comprehensive understandings on geological phenomena.

Acknowledgments This study is financially supported by a Grant-in-Aid for Scientific Research of the Ministry of Education and Science, Japan (18404020). ESRI granted the ArcGIS 8.3 for the senior author for initiating this project, while the current software version of ArcGIS 9.1 is kindly supported by Prof. Tetsuro Esaki from Environmental Geotechnology Laboratory, Kyushu University. Authors thank two anonymous reviewers for their constructive suggestions.

References 1. M. Zeiler, Modeling Our World: The ESRI Guide to Geodatabase Design (ESRI Press, Redlands, 1998), 198 p. 2. P. A. Longley, M. F. Goodchild, D. J. Maguire and D. W. Rhind, Geographic Information Systems and Science (John Wiley and Sons, West Sussex, 2001), 454 p. 3. S. Grise and B. Brodaric, http://support.esri.com/datamodels (2003). 4. North American Geologic-map Data Model Steering Committee, http:// geology.usgs.gov/dm/ (2003). 5. S. M. Richard, D. R. Soller and J. A. Craigue, http://support.esri.com/ datamodels (2005). 6. L. D. Setijadji and K. Watanabe, Res. Geol. 55 (2005) 267 . 7. L. D. Setijadji, K. Watanabe, R. Wahyuningsih and D. Wintolo, Proc. World Geoth. Cong. (2005). R 05-087r4 (2006). 8. S. Cox (ed), OGC Best Practices OGC
9. G. L. Raines, J. T. Hastings and L. A. Moyer, http://support.esri.com/ datamodels (2007).

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Advances in Geosciences Vol. 13: Solid Earth (2007) Ed. Kenji Satake c World Scientific Publishing Company


CRUSTAL DEFORMATION MONITORING BY GNSS: NETWORK ANALYSIS AND CASE STUDIES MARCO PIRAS∗ and MARCO ROGGERO DITAG, Politecnico di Torino C.so Duca degli Abruzzi 24, Torino, 10129, Italy ∗ [email protected] MAURIZIO FANTINO NAVSAS, Istituto Superiore “M. Boella” C.so Castelfidardo 30/A, Torino, 10129, Italy

GNSS have been widely used to monitor crustal deformation with a high precision. In this chapter, the workflow of deformation monitoring by continuous GNSS observations is addressed. First, the permanent GNSS network characteristics and structure are described. Second, the GNSS observation data are processed by the Bernese software and corresponding solutions of GNSS coordinate time series are analyzed in detail at the time domain. Finally, the GNSS solutions are used to detect the deformation, and some case studies are presented.

1. Introduction The thin and deformable Earth’s crustal layer is subject to endogenous and exogenous agents, dominated by gravitation. Land deformation involves phenomena characterized by the long-term crustal movements and local short-term displacements. From a practical point of view, the study of the long-term phenomena requires that the geodetic observations are not affected by local displacements. Moreover, for engineering applications, monitoring of local deformations is required. Global Navigation Satellite Systems (GNSS) techniques can be used in both cases, jointly with other geodetic techniques. If GNSS systems are the useful tools to investigate the Earth’s deformation, they are also employed as monitoring systems for local deformation. The goal of local phenomenon monitoring above all is to detect movements and then to estimate the entire deformation and direction. 87

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The local site can be instrumented with GNSS systems, and a control center saves raw data and estimates the solution in near-real time or postprocessing mode. As an alternative solution to the continuous operating network is set up, the site can be monitored by GNSS survey campaigns repeated at fixed intervals; this solution is most frequently chosen in the case of small areas, such as landslide monitoring. In the last several years, a number of permanent stations has increased, although not uniformly, on the local, regional, and global network scale. GNSS’ continuously operating networks have different purposes, both geodetic and geodynamic or, more often at the national or local level, which are designed for supporting topographic operation also in real time. Owing to recent densification of GNSS arrays in boundary zones of tectonic plates, there is an increasing demand for computational programs incorporating realistic theoretical models. Recent studies include surface deformation stemming from dislocation at depth, crustal movements due to various loads, including jumps, periodic components and transient phenomena, incorporation of visco-elasticity, combination of data from different satellite geodesy techniques, Laser Ranging, VLBI, and DORIS. Such studies are followed by several steps, like GNSS time-series analysis, tide gauge, linear regression, identification of jumps, search set of faults or inflation sources, inversion of complicated slip distribution, temporal evolution of slips, modeling post-seismic transients, drawing diagrams, and so on. Herein, in this chapter, attention will be focused on the GNSS data production and analysis, following the workflow from the network setup, up to the coordinate time series analysis. Several case studies will be presented with permanent GNSS networks and repeated survey campaigns.

2. GNSS Monitoring Network and Data Processing The aim of GNSS techniques for monitoring crustal deformations is to distinguish from geodetic signals, which are generated by different sources and mixed in the coordinate time series. To realize that, we need: • • • •

to set up a GNSS network, carefully designed and controlled; to use a reliable connection between the observation and the coordinates (correct datum definition and realization, models, and conventions); to check for clean and coherent results (remove outliers, systematic observation effects, datum effects); to detect and remove time series discontinuities, long-term linear trend and seasonal effects.

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Monitoring networks should be analyzed according to three criterias: accuracy, reliability, and sensitivity. Accuracy describes the quality of the network in terms of random errors. Reliability is defined as the ability of the network to sense and identify gross errors in the measurements, as the undetected influence of gross errors in a monitoring network can be easily interpreted as a deformation, thus resulting in an undesirable situation. The sensitivity of a monitoring network is defined as its own capacity to detect and measure movements and deformations in the area covered by the network itself. The geological phenomenon is monitored and its parameters are used to analyze the sensitivity of a network. In a deformation area, the possible deformation model can be predicted according to information derived from geological and seismic surveys and geodetic results. The network sensitivity can be defined with respect to the probability to detect a geological phenomenon; if it occurs, it can be detected with the probabilities α and β (α being the level of significance and β the test power, as specified by the Baarda’s reliability theory).

2.1. GNSS continuously operating network setup The sensitivity of the network, defined as its capacity to detect and measure movements and deformation in the said area, is a crucial criterion. It is therefore suggested that the network should be designed according to the geological phenomena to be monitored. The GNSS baselines configuration design may be carried out mainly by the sensitivity criterion. With prior information, it is possible to design the network according to a defined deformation model. This configuration will then contain the vectors with the most effective contribution to the network sensitivity. The proposed site location must be explored afterward, in order to find the suitable location for station installation, where a number of other technical and logistic factors have to be considered (bedrock type, sky view, background noise, safety, accessibility, power availability, and data transmission). Preliminary GNSS campaign and analysis of the observations’ quality control (QC) are the necessary steps for evaluating the candidate sites (Fig. 1), at first selected on the basis of geophysical, geological, or geotechnical requirements. This operation is performed collecting raw data on the candidate site for 48 h and then analyzing the observations following the proper quality standards (UNAVCO).

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Fig. 1.

Skyplot of multipath.

UNAVCO, a non-profit, membership-governed consortium, supports and promotes Earth science by advancing high-precision techniques for the measurement and understanding of deformation. Most of UNAVCO’s members are geophysical geodesists who study deformation. The primary tool supported by UNAVCO has been GPS. However, UNAVCO is moving toward including support for other techniques useful for studying deformation. Borehole strainmeters, Interferometric Synthetic Aperture Radar (InSAR), and Light Detection and Ranging (LIDAR) are expanding the spatial and temporal signals that can be investigated with geodetic techniques. At the same time, GPS is finding applications in a frequency range that used to be the sole provenance of seismology, as GPS moves from one solution per day to one solution per second. UNAVCO is also expanding its role in education. These changes in UNAVCO are part of a conscious strategy to meet the future needs of the science community supported by UNAVCO. During the past year, UNAVCO developed a Strategic Plan to guide it through the next 5 years.

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If the sites satisfy the quality requirements, some logistic aspects have to be evaluated for the power supply and data link, and finally the access for the station maintenance and also the security against vandalism acts. The power supply is usually realized easily by photovoltaic cells and accumulators, and the major technical problems are often related to the data link. GSM modems and radio modems are the most frequently practiced solutions, even though in local networks, such as in landslide monitoring, it can be more convenient to set up a wireless LAN. This is possible thanks to the capability of modern GNSS receiver to communicate through the Ethernet protocol. The monumentation has to be studied for providing a good stability minimizing the realization costs (Fig. 2). Setting up a permanent station for deformation monitoring, it is usually necessary to anchor the monument directly on the bedrock, with the exception of stations situated on sedimentary layers. However, the bedrock can be covered by soil layers, with a thickness of some meters, or can be fractured by faults. In such cases, preliminary geological and geotechnical studies are necessary for evaluating the site-supposed local stability and the necessary structural solutions to set up the station. Finally, the economical aspects can suggest to choose another site.

Fig. 2.

Monumentation’s map.

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A low data sampling rate (i.e. 15 s) is enough to support most of the applications for a deformation monitoring network, with the advantage of requiring a low bandwidth for the data transmission. Higher data sampling rate, up to 1 Hz, is desirable to support specific GPS studies, such as investigation of ionospheric scintillations and the water vapor tomography during occultations of Low Earth Orbiters (LEO) with GPS receivers on board. However, there is growing interest in collecting data with high rates also for a variety of geodynamic applications. GPS measurements can accurately track the propagation of earthquake dynamic motions both on the ground and in the atmosphere, providing complementary information to seismic observations and estimates of Earth structure.

2.2. Monumentation The international standards for permanent GPS stations involved in geodynamical studies, and the fact that tectonic displacements are often of the order of only a few millimeters per year, require to achieve the highest possible stability of the monument. Moreover, to detect crustal deformation, it is underlying to be able to distinguish the land deformation from the monumentation deformation (temperature excursion, concrete fluage, wind, etc.), or to minimize them. Usually, the most used monumentation is the concrete pillar, preferably anchored directly on the bedrock. However, different solutions have been proposed, the most diffused are: • • • •

concrete pillar (UNAVCO rebar and C-bar reinforced concrete pillar) metallic pillar (UNAVCO stainless steel) tripod (UNAVCO short-deep drilled braced) steel mast.

The very common practice of installing stations on building roofs is in this case not advisable, because the observations could be affected by oscillations or seasonal building displacements related to thermal expansion. A research overview conducted in Italy, analyzing different Italian GNSS networks designed both for topographic and geodynamic purposes, shows that the stations monumented directly on the ground are 43%, while 57% are monumented on top of buildings. The 77% of the station monuments on the ground are concrete pillars (33% of the total), 12% steel tripod, and 7% steel pole; the remaining 4% are steel mast.

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Apart from the different opinions in the geodetic community about the better monumentation (concrete pillar or steel tripod), the following guidelines must be followed in permanent GNSS station setup. The monument must be well anchored into solid bedrock, and in order to define whether exposed rocks have optimal features, it is mandatory to perform inspections with a geologist. The anchorage between pillar and bedrock must be done by means of iron bars of suitable suitability section, which must be inserted into the ground to 2–3 m deep. The iron bars, which are usually fixed to the bedrock by means of special glues, have to emerge from the ground to allow the coupling of the monument’s structure. The monument height should be about 150 cm, or in any case greater than the average registered snow height. The receiver antenna must be linked to the monument by means of an iron mount device, leveled upon installation.

2.3. Local control network Site effects and monument stability at permanent GNSS stations are controlled by monitoring a local control network, formed by three or more GNSS and leveling points. These control points can be materialized on subhorizontal rock surfaces by means of steel geodetic markers. Where it is not possible to find suitable surfacing bedrock, the control points can be set up on the foundations of massive and stable buildings. In some cases, only subvertical surfaces are available to set up the control points, or the only possible locations for the control points are not suitable for acquiring GNSS observations: in these cases the control points can be used only for leveling. The distance of the control points from the GNSS station depends on the wavelength of the local phenomena to be monitored. Moreover, this distance must be a compromise between different factors such as: bedrock availability, GNSS satellite visibility, time necessary to reach the control point or to perform the leveling, chance to preserve the control point. Local control network setup and monitoring are of primary importance. However, the monitoring is quite expensive in terms of time and human resources in the field, and a monitor survey campaign repeated once per year can be a good compromise. An additional very important role of the control network is represented by the possibility to determine on-site the previous location of the GNSS antenna, in case the pillar is damaged or destroyed by natural causes, accidents, or acts of vandalisms. If it would ever be necessary to substitute

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or relocate the monument, in fact, control points are the only way to link the new antenna location to the previous position and avoid wasting previous measurements. For referencing reasons, it is very important that the control points are also observed by GNSS.

2.4. Data processing Briefly, the different purposes of the data analysis could be summarized as follows: • • • •

integration of the monitoring network with the global network and with neighboring networks; data quality control, by means of automatic procedures; production of coordinate time series, to be analyzed for the deformation monitoring; production of ionosphere and troposphere estimates.

In order to determine station positions and velocities of the entire monitoring network, a unique and consistent reference system has to be used in all steps of data processing for coordinate determination. Position coordinates refer to a coordinate system that has to be defined coherently, and velocities need a stable origin to which the motions refer. The reference system is realized in practice by a reference frame, which is a set of reference stations with coordinates according to the definition of the reference system. The reference frame is broadcasted to the user by station coordinates, ephemeris, and related products. Due to crustal deformation (tectonic plate motions and regional deformations), the coordinates of the stations change with time relative to the satellite orbits. Therefore, we have to transform the coordinates of the reference frame by the station velocities to the observation epoch in the data processing.1 The reference system for the coordinates of the satellites and of the ground stations must be the same, and needs a dense spacing in the region of the measurements. This is only fulfilled by the ITRF, at present in its realization ITRF2005, consisting of a table of station coordinates and velocities at a reference epoch t0 = 2000.0, under the hypothesis of constant velocities. A GPS-based ITRS realization series, IGS05, is managed by the IGS, and distributed via ephemerides (GPS and GLONASS) and Earth Orientation Parameters (EOP). In IGS05, the GPS receiver phase center variations are corrected according to the IGS conventions of 2006. Both ITRF2005 and IGS05 realizations are characterized by position mean

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standard deviations at the millimeter level. Nevertheless, discrepancies between the ITRF and IGS (long-term and weekly solutions) realizations may be as large as several millimeters. IGS realization, even if not more accurate, is indeed more consistent with GNSS observations. For this reason it seems more rigorous to perform the network adjustment in the IGS datum, and all the standards used for the computation of the IGS global network must be used in the adjustment, if applicable. The connection with the ITRF is known and the transformation parameters are published by the IGS. It must be noted that the variances and covariances of the coordinates of the IGS weekly solutions are quite underestimated, and therefore it is not correct to use them in the stochastic model for the adjustment of the PN. Therefore, the weekly solutions must be used through a procedure that includes the estimates of their variances and covariances from the time series of the coordinates. In order to get a dense terrestrial reference frame, an ITRF/IGS densification is necessary for the IGS network non-uniform distribution. The GNSS data-processing strategy depends on the software used, and can be defined through models, products, and procedures. It is necessary to define satellite orbits and Earth rotation parameters, antenna phase center variation, ionospheric and tropospheric models, ocean tide loading, ambiguity resolution strategy, cut-off angle, datum, and so on. The final products, including station coordinates, depend on all these definitions and models.4

3. Time Series Analysis The most important results of the data analysis are the daily coordinates of the GNSS Permanent Stations. The long-term time series of daily solutions are then analyzed to get estimates of the linear station velocities, which are a basic input for geodynamic interpretation. A detailed time-series analysis however must be performed before estimating the station velocities. The analysis of the noise characteristics and of the periodic signal allows a deeper insight into some processes in the network and at the station’s environment as well. An appropriate statistical analysis of the colored noise allows a more reliable velocity uncertainty estimation and may provide information on the monument stability. The periodic seasonal signals may come from real physical sources (e.g. atmospheric and hydrologic effects), and also mismodeling or local environmental effects (e.g. multipath, thermal effects) may contribute to the periodic coordinate variations. Coordinate time-series

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analysis requires the computation of a cumulative (multi-year) solution resulting in clean time-series plots, a catalog with the station coordinates and velocities, and a catalog with the coordinate offsets and outliers. Then it is possible to determine realistic coordinate and velocity uncertainties by estimating the spectral index of the noise of the coordinate time series, and finally the harmonic analysis of the coordinate time series can be performed by estimating the seasonal periodic term and providing de-trended, cleaned time-series plots. The harmonic analysis of GNSS coordinate time series shows seasonal variation of the station coordinates, usually with an annual or semi-annual period. The distribution of the amplitudes and phases is often fairly random and hard to interpret as a real geophysical signal. Rather, they reflect the modeling shortcuts of antenna phase center variations (PCV) and environmental impacts (multipath, thermal effects). Discontinuities in the time-series analysis can also occur in case of Reference Frame change or models change. Even though coordinate transformation is applied with proper parameters, residual discontinuities can be present after datum shift compensation. The residual discontinuities are further estimated and removed from the time series. The first results of the comparison between IGS05 (with absolute PCV) and IGb00 solutions (with relative PCV) largely support the importance of the introduction of the absolute PCV models and then the general reprocessing of the whole GPS observation material using the new ITRF2005 frame and the new PCV models (Fig. 3). Other documented jumps are caused by hardware change, such as of antenna, receiver, or site modifications, but undocumented jumps remain. The main problem during the time-series analysis is to detect and remove these undocumented jumps, in order to obtain refined time-series suitable for reliable velocity estimates. Different algorithms can be used to detect and estimate the discontinuities in time series as traditional method (i.e. Least Squares) or robust method (i.e. Huber method) or sequential approach (i.e. Kalman filter2,3 ). After the jumps (documented and undocumented) are removed, the station velocities can be estimated, and the long-term linear trend can be removed. This is fundamental for the frequency analysis, the purpose of which is to recover principal signals without a priori hypothesis on their period and amplitude. The sampling of the time series is usually non-uniform due to data gaps, so that it is not possible to realize the frequency analysis using the FFT.

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Example of coordinate time series with some discontinuities.

However, other algorithms can be suitable, such as FAMOUS — Frequency Analysis Mapping On Unusual Sampling, by F. Mignard (OCA/Cassiopee). Figure 4 shows different kinds of information on seasonal and annual effects and the long-term linear trend. The vertical line indicates the reference frame changes, in particular from IGS97 to IGS00 (at the left) and from IGS00 to IGS05 (at the right).

4. Some Case Studies 4.1. The Sumatra–Andaman earthquake The first case study concerns the drastic phenomenon of Sumatra–Andaman earthquake, which occurred in 2004. Long-term GPS time-series analysis shows that the Australian and Indian plates move toward Sumatra– Andaman at the velocities of 5 cm and 4 cm per year, respectively. These results are derived from a large network of about 200 permanent GPS stations, routinely analyzed for various research purposes, including the

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Seasonal and annual effects estimated using the FAMOUS algorithm. Fig. 4.

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monitoring of global sea level, climate change, and crustal deformation in Southeast Asia, Australia, the South Pacific, and Antarctica. Different displacements have been detected during the event of 2004 by GPS, and the entity and velocity of displacement vary with location. Displacements vary from 3 to 6 m in the Andaman and Nicobar Islands. An almost 28 cm displacement was detected at GPS site PHUK (Phuket Island, southern Thailand near northern Malaysia), decreasing gradually from the north to the south. In this analysis, the GPS data can detect coseismic, kinematic, and post-seismic deformation. Co-seismic displacements were computed using two combined seven-day solutions — one before and one after the earthquake. Displacements of the sites were calculated as the differences between the two solutions. The co-seismic deformation at GPS site ARAU (Perlis, northern Malaysia) are about 15 cm in the east and 3 cm in the north. The southeast sites seem not to have been impacted by the earthquake, which implies that the stress along the subduction zone plate interface of southern Sumatra was not released. This is the likely reason for the Simeulue–Nias earthquake on 28 March 2005. The determination of co-seismic deformation is very useful for further investigation of fault slip models and of other seismic features of the earthquake.6 Kinematic coordinate solutions, computed from stations near the earthquake every 30 s over 30-min period before and after the earthquake (0:59, 26 December 2004), show the progression of the rupture. For example, GPS site ARAU had a deformation of 10 cm. Deformation was detected when the surface waves began to hit the site 2 min after the earthquake; 4 min later, positions at the site were relatively stable again. Using a long-term GPS time series after the earthquake, we can also examine the post-seismic deformation process. An eastward deformation of more than 6 cm was determined during the 80-day period after the earthquake. Such post-seismic deformation information from all available GPS sites in the earthquake region can help scientists analyze likely elastic, poroelastic, and viscoelastic deformation, and plastic flow of the Earth’s crust in the earthquake region, giving a better understanding of crustal relocation and redistribution after the earthquake. 4.2. Crustal deformation in Japan The Geographical Survey Institute (GSI) has established the GPS control network with about 1224 control points throughout the country, which is designed to monitor crustal movement in real time.

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Crustal movements are not only caused rapidly by earthquake swarm or big earthquake, but also slowly by plate motions or magma activities. It is necessary to detect ongoing crustal deformation with high precision for researches of earthquakes and volcanic eruptions. The Geographical Survey Institute (GSI) conducts baseline analysis every day using 24 h GPS observation data; therefore, it is possible to detect crustal deformation continuously. GPS data is expected to contribute to the prediction of Tokai Earthquake that is anticipated in near future, and to the studies of an earthquake mechanism.

4.3. Crustal deformation in the Alps Along the Alps there are two different geodetic GNSS networks, devoted to monitoring the Alps deformations: the first one is REGAL, installed in 1997 by G´eosciences Azur and the second one is FReDNet operated by The Istituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS), which monitors the deformation along the northeast boundary of the Adriatic microplate (∼5 mm/yr north–south convergence). Other permanent stations have been installed in the context of the RING network, managed by the Istituto Nazionale di Geofisica e Vulcanologia (INGV). There is, besides, a transnational network called GAIN (Geodetic Alpine Integrated Network) installed during 2004–2006, funded by the EU Alpine Space III-B Program, which involves several research and environmental institutions standing within the Alpine Space (Fig. 5). The aim of the project is to build up a high-performance space geodetic network of continuous GPS (CGPS) receivers in the Alps and a number of campaign subnetworks. The Geodetic Alpine Integrated Network consists of more than 35 CGPS and will be the first ever installed transnational geodetic network across the Alps. It will delineate the seismogenic potential within the Alpine space, mountains, and surrounding foothills, where concentrated attractive European metropolitan areas and rapidly growing urban centers with extensive infrastructures are present. It will favor transnational know-how exchange between regional authorities and Alpine universities and research centers. It will reinforce the European Space geodetic and geoscience communities.5 This will open new research initiatives in earth and environmental sciences, therefore rising the value of the Alps as a natural laboratory. The direct result will be an improvement in the knowledge of earthquake

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Geodetic Alpine Integrated Network (GAIN).

potential and hazard, and this will allow a better land use in terms of a safe living space. GAIN will give ground for a higher resolution space-based coverage of urban and mountain areas in the Alpine space, better resolve satellite imagery, and therefore offers a robust tool for future infrastructure investment, land use harmonization, and industrial planning. GAIN will be integrated with highly resolving remote-sensing methods (e.g. InSAR) and will give new potentials to monitor and prevent environmental degradation and limit the impacts of natural disasters. First results of the GAIN network are presented, focusing on the problem of misinterpretation of too short time series. In the case of the Alps, the deformations are not as large as in the previous examples, and we need to observe very long time series. The dominant source of horizontal deformation is caused by plate tectonics. It is therefore natural to remove this signal for studying the remaining deformation. A well-known and generally accepted model for plate tectonics is the NNR-NUVEL-1A model, based on a geological and geophysical data. The remaining horizontal velocities are now much smaller and the differential deformation signals, which are not caused by the plate tectonics of the Eurasian plate, are visible. The actual estimation of these horizontal velocities does not show a certain pattern and is rather randomly distributed. But this should not be misinterpreted, since the time series

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of the position changes is rather short, at the maximum of 2 years. It is quite typical to see seasonal variations in the time series of the coordinate components. The origin of these signals is yet not well understood. It is therefore necessary to observe the stations for several years. Then, the seasonal signal averages out and does not affect the velocities. 5. Conclusions GNSS nowadays is a reliable technique for monitoring deformations, but it still requires some improvements in data-processing and analysis. We underlined some considerations about the main activities and the problems still to be solved. Analyzing coordinate time series, we need to detect the non-tectonic motion (e.g. documented and undocumented jumps), the long-term linear trend (station velocity), and the periodic components (annual–semiannual). Known discontinuities must be reported in log files, but we cannot be sure that all changes are always documented and that all metadata are updated. Moreover, the source of undocumented time-series discontinuities must be investigated. The long-term linear trend represents the station velocity with respect to the reference frame, constant or not after an earthquake; the long-period signal represents the non-linear part of the deformation, and it needs very long time series to be detected (>10 years). The main difficulty is to verify the reliability of the estimated deformations, and to explain their physical causes (tectonic drift, seasonal effects, etc.). Finally, the growing number of permanent GNSS monitoring networks with numerous international cooperation, is increasing the resolution of crustal deformation knowledge, allowing the development of more realistic geophysical models.

References 1. W. Baarda, A testing procedure for use in geodetic networks, Publ. Geodesy 2(5) (1968), Netherlands Geodetic Commission, Delft, ISBN-13: 978-90-6132209-2. 2. R. E. Kalman, A new approach to linear filtering and prediction problems, Trans. ASME J. Basic Engr. (1960) 35–45. 3. Y. J. Wang and K. K. Kubik, Robust Kalman filter and its geodetic applications, Manuscripta Geodetica 18 (1993) 349–354. 4. M. Crespi, A software package for the adjustment and the analysis of GPS control networks, Reports on surveying and geodesy (1996), pp. 237–264.

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5. G. Even-Tzur, Sensitivity design for monitoring deformation network, Bollettino Di Geodesia E Scienze Affini 58 (4) (1999) 313–324. 6. J. Minghai, Crustal deformation from the Sumatra-Andaman earthquake, AusGeo News No. 80 (2005).

Web References GAIN http://www.alps-gps.units.it/ IGS http://igscb.jpl.nasa.gov/ EPN http://www.epncb.oma.be/ Japan http://mekira.gsi.go.jp/ENGLISH/

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Advances in Geosciences Vol. 13: Solid Earth (2007) Ed. Kenji Satake c World Scientific Publishing Company


THE GLOBAL GEODETIC OBSERVING SYSTEM H.-P. PLAG∗ , M. ROTHACHER† , M. PEARLMAN‡ , R. NEILAN§ and C. MA¶ ∗University of Nevada, Reno, Nevada Bureau of Mines and Geology and Seismological Laboratory Mail Stop 178, Reno, NV 89523, USA [email protected] http://geodesy.unr.edu/ †GeoForschungZentrum

Potsdam Potsdam, Germany

‡Harvard-Smithsonian

Center for Astrophysics Cambridge, Massachusetts, USA

§Jet

Propulsion Laboratory, Pasadena, California USA ¶Goddard

Space Flight Center Greenbelt, Maryland, USA

The Global Geodetic Observing System (GGOS) was established by the International Association of Geodesy (IAG) in July 2003. In April 2004 the IAG, represented by GGOS, became a participating organization of the Group on Earth Observation (GEO) and in May 2006 GGOS was accepted as a member of the Integrated Global Observation Strategy Partnership (IGOS-P). GGOS is the contribution of geodesy to the Global Earth Observation System of Systems (GEOSS). It provides the reference systems and frames, which are crucial for Earth observing systems. GGOS is built on the IAG Services (IGS, IVS, ILRS, IDS, IERS, IGFS, etc.) and the products they derive on an operational basis for Earth monitoring, making use of space- and groundbased geodetic techniques such as Very Long Baseline Interferometry (VLBI), Satellite and Lunar Laser Ranging (SLR/LLR), Global Navigation Satellite Systems (GNSS), Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS), altimetry, InSAR (Interferometric Synthetic Aperture Radar), gravity satellite missions, and gravimetry, etc. All these observation techniques are considered integral parts of GGOS, allowing the monitoring of the Earth’s shape and deformation (including water surface), the Earth’s orientation and rotation, and the Earth’s gravity field and its temporal variations with an unprecedented accuracy. The observed parameters give direct evidence of many global processes that have a crucial impact on human

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society such as earthquakes, volcanism, floods, sea-level change, climate change, groundwater redistribution, mass balance of the polar ice sheets, etc. GGOS relies on the observing systems and analysis capabilities already in place in the IAG Services and envisions the continued development of innovative technologies, methods, and models to improve our understanding of global change processes. GGOS provides a framework that ranges from the acquisition, transfer, and processing of a tremendous amount of observational data to its consistent integration and assimilation into complex numerical models of the Earth system (including solid Earth, oceans, atmosphere, hydrosphere, cryosphere, and the interactions thereof). This is being achieved by an international effort, and a close, multi-disciplinary cooperation with groups working in related fields such as geodynamics, geophysics, oceanography, hydrology, glaciology, meteorology, and climatology. In summary, GGOS provides essential contributions to an integrated Earth monitoring system to help us better understand global change and its impact on environment and society.

1. Introduction Earth is a restless planet.1 With its atmosphere, oceans, ice covers, land surfaces, and its interior, it is subject to a large variety of dynamic processes operating on a wide range of spatial and temporal scales, and driven by large interior as well as exterior forces. Many areas of the Earth’s surface are exposed to natural hazards caused by dynamic processes in the solid Earth, the atmosphere, and the ocean. Earthquakes, tsunamis, volcano eruptions, tectonic deformations, land slides, deglaciation, sealevel rise, floods, desertification, storms, storm surges, global warming, and many more are typical and well-known phenomena that are expressions of the dynamics of our restless planet. In modern times these processes are influenced, as well, by anthropogenic effects; to what extent is still largely unknown. Some of the many examples of anthropogenic effects are carbon and methane emissions, changes in soil composition and erosion rates, regulations and diversions of rivers, deforestation, and extinction of species (see Ref. 2 for an overview of the anthropogenic impact on the Earth system over the last 300 years). A growing population has to cope with this restless, and finite, planet. Settlements are encroaching into areas of high risks from natural hazards with major infrastructure being built in potentially hazardous locations, thus increasing the vulnerability of society. Increasingly, valuable and crucial infrastructure is lost in natural disasters, affecting the economy on national and global levels, and displacing large populations, with

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severe social implications. The growing demands for access to food, water, materials, and space put stress on the finite resources of the planet. Earth system processes, whether natural or modified by humans, affect our lives and the lives of future generations. Decisions made today will influence the well-being of future generations. In order to minimize the anthropogenic impact on Earth system processes and to preserve resources for future generations, a better understanding of Earth system processes and an efficient and conservative organization of anthropogenic processes is required. Responsible stewardship of the planet is not possible without a profound understanding of the processes that shape the planet. Examples are mitigation of the potential impact of climate change on ecosystems, sustainable management of the oceans, preservation of water resources for humans and the biosphere, and preparing for a potentially devastating impact of sea-level rise on coastal communities. Living on a restless planet with finite resources and a limited capacity to accommodate the impact of the increasingly powerful anthropogenic factor requires careful governance. A number of World Summits have acknowledged that finding a way to ensure sustainable development is mandatory for realizing a stable and prosperous future for the anthroposphere. Although there are many other influential factors, understanding the Earth system and its major processes and its trends, is one of the prerequisites for success in this quest for sustainable development. A deeper understanding of the Earth system cannot be achieved without sufficient observations of a large set of parameters characteristic of Earth system processes. As emphasized by the Earth Observation Summits (EOS), there is an urgent need for comprehensive Earth observations (see the documents in the Appendices of Ref. 3). Earth observations are not only necessary for a scientific understanding of the Earth, but are also fundamental for most societal areas ranging from disaster prevention and mitigation, the provision of resources such as energy, water, and food, gaining an understanding of climate change, the protection of the biosphere, the environment, and human health, to the building and management of a prosperous global society. Geodesy provides mandatory reference frames as a foundation for Earth observation. Moreover, geodesy observes parameters related to the mass transport in the Earth system and the system dynamics. With this, geodesy is a cornerstone in Earth observation.

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2. Geodesy’s Contribution to Earth Observations and Society at Large The “three pillars” of geodesy are the Earth’s time-dependent geometric shape, gravitational field, and rotation (Fig. 1). With its observational means (Table 1), geodesy has the potential to determine and monitor with utmost precision the geometric shape of land, ice, and ocean surfaces as a global function of space and time. The geometric methods, when combined with global gravity information and the geoid, allow us to infer mass anomalies, mass transport phenomena, and mass exchange in the Earth’s system. The variations in Earth rotation reflect mass transport in the Earth system and the exchange of angular momentum among its components. The geodetic observations of the “three pillars” provide the basis for the realization of the reference systems that are required in order to assign (time-dependent) coordinates to points and objects, and to describe the motion of the Earth in space (Fig. 1). For this purpose, two reference systems are basic in geodesy, namely the celestial reference system and the terrestrial reference system, which are dynamically linked to each other by the Earth’s rotation. The two most accurate reference systems currently available are the International Celestial Reference System

Fig. 1. Constituents of an integrated geodetic monitoring system. The “three pillars” of geodesy provide the conceptual and observational basis for the reference frames required for Earth observation. Moreover, these “three pillars” are intrinsically linked to each other as they relate to the same unique Earth system processes. Today, the spacegeodetic techniques and dedicated satellite missions are crucial in the determination and monitoring of geokinematics, Earth’s rotation, and the gravity field. Together, these observations provide the basis to determine the geodetic reference frames with high accuracy, spatial resolution, and temporal stability. (From Ref. 5 modified from Ref. 6).

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Table 1. Observing the “three pillars” of geodesy and maintaining the global geodetic reference frames. GGOS utilizes a large set of space-geodetic, airborne and in situ techniques. (For acronyms, see text and Table 2.) Component I. Geokinematics (size, shape, kinematics, deformation)

Objective

Shape and temporal variations of land–ice–ocean surface (plates, intraplates, volcanoes, earthquakes, glaciers, ocean variability, sea level) II. Earth rotation Integrated effects (nutation, of changes in precession, angular polar motion, momentum variations in (mass changes LOD) in atmosphere, cryosphere, oceans, solid Earth, core/mantle; momentum exchange between Earth system components) III. Gravity field Geoid, Earth’s static gravitational potential, temporal variations induced by solid Earth processes and mass transport in the global water cycle IV. Terrestrial Global cluster of frames fiducial points, determined at mm to cm level

Techniques

Responsible

Altimetry, InSAR, GNSS cluster, VLBI, SLR, DORIS, imaging techniques, leveling, tide gauges

International and national projects, space missions, IGS, IAS, future InSAR service

Classical astronomy, VLBI, LLR, SLR, GNSS, DORIS; underdevelopment: terrestrial gyroscopes

International geodetic and astronomical community (IERS, IGS, IVS, ILRS, IDS)

Terrestrial gravimetry International (absolute and geophysical and relative), airborne geodetic gravimetry, community (GGP, satellite orbits, IGFS, IGeS, BGI) dedicated satellite missions (CHAMP, GRACE, GOCE)

VLBI, GNSS, SLR, International geodetic LLR, DORIS, time community (IERS keeping/transfer, with support of absolute IVS, ILRS, IGS, gravimetry, gravity and IDS; BGI) recording

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(ICRS) and the International Terrestrial Reference System (ITRS), which are defined by the International Earth Rotation and Reference Systems Service (IERS). These systems are conventional coordinate systems that include all conventions for the orientation and origin of the axes, the scale, and the physical constants, models, and processes to be used in their realization. Based on observations, these systems can be realized through their corresponding “reference frames.” The frame corresponding to the ICRS is the International Celestial Reference Frame (ICRF), which is a set of estimated positions of extragalactic reference radio sources. The frame corresponding to the ITRS is the International Terrestrial Reference Frame (ITRF), which is a set of estimated positions and velocities of globally distributed reference marks on the solid Earth’s surface. These two frames are linked to each other by the estimates of the Earth rotation parameters. ICRS, ITRF, and the Earth rotation parameters are provided by IERS. Today, the internationally coordinated geodetic observations collected and made available by the global geodetic station networks provide a continuous monitoring of the ITRF. This well-defined, long-term stable, highly accurate, and easily accessible reference frame is the basis for all precise positioning on and near the Earth’s surface. It is the indispensable foundation for all sustainable Earth observations, in situ, as well as airborne and space-borne. Furthermore, the ITRF underpins all geo-referenced data used by society for many uses. All these digital geo-referenced data are crucial for many activities, including mapping, construction, land development, natural resource management and conservation, navigation — in fact all decision-making that has a geo-related component. Historically, geodesy was limited to determining the Earth’s shape, gravity field, and rotation including their changes over time. With modern instrumentation and analytical techniques, the scope of geodesy can be extended to include the sources of the observed changes, that is, the dynamics of and mass transport within the Earth system.4 With this broader scope new pathways emerge in which geodesy can contribute to the scientific understanding of the Earth system as well as the development, functioning, and security of society in general. Ultimately, the observations in these “three pillars” are affected by the same unique Earth system processes: all of them relate to mass redistribution and dynamics (Fig. 2). Thus, geodesy provides a unique framework for monitoring and ultimately understanding the Earth system. Modern space-geodetic techniques are well suited for observing phenomena on global to regional scales, and thus are an important complement to traditional in situ observation systems.

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Fig. 2. Mass redistribution in the Earth system. All geophysical processes are associated with mass redistribution and changes in the dynamics, thus affecting commonly the Earth’s gravity field, geometry, and rotation. Consequently, geodesy with observations of the “three pillars” contributes to an observing system that allows the monitoring of mass transport in the Earth system. (From Ref. 7).

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Many scientific applications depend on detailed knowledge of the Earth’s shape, its gravity field, and rotation. In the past, geodesy has, with ever-increasing accuracy, provided the necessary observations, although with many limitations in accuracy and spatial coverage. The fairly recent advent of space-geodetic techniques has brought about a rapid development in global geodesy, particularly during the last decade or so. The relative precision of the measurements is approaching the very impressive level of 1 part-per-billion (ppb) or even better. Today, geodetic techniques permit the measurement of changes in the geometry of the Earth’s surface with an accuracy of millimeters over distances of several 1,000 km. Over the last one and a half decades, the global geodetic networks have provided an increasingly detailed picture of the kinematics of points on the Earth’s surface and the temporal variations in the Earth’s shape. Among other applications, the observations have been used to determine improved models of the secular horizontal velocity field (e.g. Refs. 8–10), to derive seasonal variations in the terrestrial hydrosphere,11 to study seasonal loading,12 to invert for mass motion,13 and to improve the modeling of the seasonal term in polar motion.14 Geodetic techniques provide the means to observe surface deformations on volcanoes,15 –17 in unstable areas,18 associated with earthquakes and fault motion,19 –21 or subsidence caused by anthropogenic activities such as groundwater extraction.22 Current developments indicate that geodetic observing techniques will be able to determine the magnitude of great earthquakes in near-real time and thus help mitigate the problem of low initial magnitudes estimated by seismic techniques.23 The space-geodetic techniques and methods also enable auxiliary applications that utilize the atmospheric disturbance of geodetic measurements (ionosphere, troposphere, magnetic field) for non-geodetic applications. The distortions of geodetic microwave signals propagating through the atmosphere can be inverted and used for weather prediction,24–26 climate studies, and studies in atmospheric physics. Air temperatures retrieved from GNSS radio occultation technique provide new and near-complete coverage of the Earth’s atmospheric mass field in the upper troposphere and stratosphere, complementing passive measurements from existing infrared and microwave sounders. Water vapor in the lower troposphere is relevant for forecasts of precipitation, while water vapor in the upper troposphere is the largest contributor to the atmospheric greenhouse effect. Thus, the geodetic techniques provide observations relevant for numerical weather forecast as well as climate studies.

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To a large extent, geodesy is a “service science,” and as such, the development of the geodetic observing system should be guided by the requirements of its users.27 In the past, the main “customers” of geodesy came from the surveying and mapping profession, while today geodesy serves all Earth science, including the geophysical, oceanographic, atmospheric, and environmental science communities. Geodesy is also indispensable for the maintenance of many activities in a modern society. Traditionally, geodesy has served society by providing reference frames for a wide range of practical applications from regional to global navigation on land, sea, and in air, construction of infrastructure, to the determination of reliable boundaries of real-estate properties. Reference frames were, however, national or regional in scope, and they were suited for the determination of coordinates relative to a network of reference points. Thus, determination of precise point coordinates required simultaneous measurements at several points. Today, the Global Navigation Satellite Systems (GNSS) provide access to precise point coordinates in a global reference frame anytime and anywhere on the Earth’s surface with centimeter-level accuracy and without requiring additional measurements on nearby reference points. On the user side, this technological development has stimulated new applications demanding even greater accuracy and better access to geodetically determined positions. On local to regional scales, applications such as land surveying, monitoring of infrastructure, prevention and mitigation of impacts of environmental hazards, and numerous technical applications require more or less instantaneous access to geodetic positions in a reliable reference frame with centimeter-level accuracy or better.27 New developments and applications will lead to increasing dependence on the geodetic foundation, that is, the terrestrial geodetic reference frame including easy access to this frame in the form of accurate positions. Geodesy has the potential to make very important contributions to the understanding of the state and dynamics of System Earth, particularly if consistent observations of the “three pillars” can be provided on a global scale with a precision at or below the 1 ppb level, and with sufficient stability over decades. A prerequisite to exploiting the full potential of geodesy for Earth observation, Earth system monitoring, and many practical applications is a sophisticated integration of all geodetic techniques (spaceborne, airborne, marine, and terrestrial), processing models, and geophysical background models into one system. The integration of the “three pillars” will permit — as part of global change research — the

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assessment of surface deformation processes and the quantification of mass anomalies and mass transport inside individual components (such as ocean, cryosphere, terrestrial hydrosphere, and atmosphere), and mass exchange between the components of the Earth’s system. These quantities serve as input to the study of the physics of the solid Earth, ice sheets and glaciers, hydrosphere, and atmosphere. They are of particular value for the study of complex phenomena such as glacial isostatic adjustment, the evolution of tectonic stress patterns, sea-level rise and fall, the hydrological cycle, transport processes in the oceans, and the dynamics and physics of the atmosphere (troposphere and ionosphere).

3. The Global Geodetic Observing System: The Organization and the System The international cooperation fostered by the IAG has led to the establishment of the IAG Services that provide increasingly valuable observations and products not only to scientists but also for a wide range of non-scientific applications (Table 2). With the recent developments in geodesy, Earth observations, and societal needs in mind, the IAG has established the Global Geodetic Observing System (GGOS) as the observing system of IAG. After a preparatory phase which included the IAG Symposium on Integrated Global Geodetic and Geodynamic Observing System in Munich in 1998,28 GGOS was initially created as an IAG Project during the IUGG meeting in 2003 in Sapporo, Japan. After devoting the first two years to the definition of the internal organizational structure of GGOS and its relationship with external organizations (the “Design Phase”), the Executive Committee of the IAG at its meetings in August 2005 in Cairns, Australia, decided to continue the Project. In the “Implementation Phase” from 2005 to 2007, the GGOS Steering Committee, Executive Committee, Science Panel, Working Groups, and Web Pages were established, and the Terms of Reference were revised. Finally, at the IUGG meeting in 2007 in Perugia, Italy, IAG elevated GGOS to the status of a full component of IAG as the permanent observing system of IAG. It is important to note here that “GGOS” has two very distinct aspects, which should not be confused: (1) the “organization GGOS” consisting of components such as committees, panels, working groups, etc., and (2) the “observation system GGOS” comprising the infrastructure of many different instrument types, satellite missions, and data and

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Table 2. The IAG Services. The IAG Services are the backbone of GGOS, providing the infrastructure for observations, data archiving and processing, and generation of products. Pillar

Acronym

Systems, Frames, Rotation

IERS

Geometry Solid Earth

IGS

IVS

ILRS IDS

Geometry Ocean PSMSL and Ice IAS

Gravimetry

IGFS

BGI IGeS ICET Standards

BIPM IBS

Service

Technique(s)

International Earth Rotation and Reference Systems Service International GNSS Service

Combination of all geometric techniques

International VLBI Service for Geodesy and Astrometry International Laser Ranging Service

Very Long Baseline Interferometry (VLBI)

International Doris Service

Doppler Orbitography and Radiopositioning Integrated by Satellites

Permanent Service of Mean Sea Level International Altimetry Service (in preparation) International Gravity Field Service

Tide gauges

Global Navigation Satellite Systems (GNSS), particularly Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), and in future Galileo

Satellite Laser Ranging (SLR), Lunar Laser Ranging (LLR)

Satellite altimetry

Gravimetric in situ (absolute and relative), airborne, and spaceborne techniques

Bureau Gravimetric Gravimetric techniques International International Geoid Gravimetric techniques Service International Center for Earth tide gravimeters Earth Tide Bureau International des Poids et Measures IAG Bibliographic Service

analysis centers. While GGOS as an organization has established, and is extending, its structure from essentially new entities, the observational infrastructure for GGOS as the system is being largely provided by the IAG Services.

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Fig. 3. Organizational links and relationships of GGOS. GGOS is being built on the scientific support from the IAG Commissions and the infrastructure of the IAG Services. GGOS integrates the work of the Services through a number of GGOS Working Groups and provides coordination and advice through its Committees. GGOS links these entities to the main programs in Earth observations, and provides a unique interface for GGOS users to the geodetic services. (Modified from Ref. 5).

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GGOS as an organization is a unifying umbrella for the IAG Services and an interface between the Services and the “outside world” (Fig. 3). Internally, the GGOS Committees, Science Panel, and Working Groups focus on cross-cutting issues relevant for all Services. By combining the “three pillars” into one observing system having utmost accuracy and operating in a well-defined and reproducible global terrestrial frame, GGOS adds to these pillars a new quality and dimension in the context of Earth system research. The observing system, in order to meet its objectives, has to combine the highest measurement precision with spatial and temporal consistency and stability that are maintained over decades. The research needed to achieve these goals influences the agenda of the IAG Commissions and the GGOS Working Groups. Externally, GGOS provides the links between the IAG Services and the main programs in Earth observations and Earth science. It constitutes a unique interface for many (although not all) users to the geodetic Services. GGOS participates on behalf of IAG in large international programs focusing on Earth observations, in particular, the Group on Earth Observations (GEO, see below). According to the IAG By-Laws, GGOS works with the IAG Services and Commissions to provide the geodetic infrastructure necessary for the monitoring of the Earth system and global change research. This statement implies a vision and a mission for GGOS. The implicit vision for GGOS is to empower Earth science to extend our knowledge and understanding of the Earth system processes, to monitor ongoing changes, and to increase our capability to predict the future behavior of the Earth system. Likewise, the embedded mission is to facilitate networking among the IAG Services and Commissions and other stakeholders in the Earth science and Earth Observation communities, to provide scientific advice and coordination that will enable the IAG Services to develop products with higher accuracy and consistency, meeting the requirements of particularly global change research, and to improve the accessibility of geodetic observations and products for a wide range of users. The IAG Services, upon which GGOS is built, benefit from GGOS as a framework for communication, coordination, and scientific advice necessary to develop improved or new products with increased accuracy, consistency, resolution, and stability. IAG benefits from GGOS as an agent to improved visibility of geodesy’s contribution to the Earth sciences and to society in general. The users, including the national members of IAG, benefit from GGOS as a single interface to the global geodetic observation system of systems maintained by the IAG Services

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not only for the access to products but also to voice their needs. Society benefits from GGOS as a utility supporting Earth science and global Earth observation systems as a basis for informed decisions. As an organization, GGOS is challenged by the recent developments in global Earth observation. The Ten-Year Implementation Plan (TYIP) for the Global Earth Observation System of Systems (GEOSS), which was prepared by the Group on Earth Observation (GEO) between 2003 and 2005, and endorsed by the Earth Observation Summit III (EOS-III) in 200529 is likely to guide the development of global Earth observation programs over the next decade. GGOS as an organization needs to be integrated appropriately into the context of Earth observation and society, and GGOS as an observing system has to be developed in accordance with the strategies and methodologies of the global observing systems for the mutual benefit of all. Earth observation and society at large will benefit from the availability of geodetic observations and products, and GGOS will benefit from an improved visibility and acknowledgment of the valuable service it provides. In order to facilitate the integration of GGOS into GEOSS, IAG is a Participating Organization in GEO (since 2004) and is represented there by the GGOS organization. GGOS is also a contributing system to the GEOSS, which is implemented by GEO. In the frame of GEO, GGOS carried out a strategy process (denoted as GGOS 2020) with the goals (1) to establish the relevant user requirements across the nine Societal Benefit Areas (SBAs) of GEO (for a list of these SBAs, see Ref. 3), and (2) to provide the basis for the implementation of a geodetic observing system that will meet the requirements of the society at large and the SBAs of GEO in particular.30 Since 2006, GGOS is a member of the Integrated Global Observing Strategy Partnership (IGOS-P)31 and is integrating its work into the Integrated Global Observing Strategy (IGOS) which is an initiative of IGOS-P. Moreover, GGOS contributes to a number of global observing systems; steps are being taken to strengthen joint initiatives with government organizations and international bodies, including relevant United Nations authorities. These initiatives have already and will continue to enhance the visibility of geodetic activities in the context of Earth sciences, Earth observation, and practical applications.27 GGOS as an observing system is built upon the existing and future infrastructure provided by the IAG Services. It aims to provide consistent observations of the spatial and temporal changes of the shape and gravitational field of the Earth, as well as the temporal variations of the

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Earth’s rotation (see Fig. 1). In other words, it aims to deliver a global picture of the surface kinematics of our planet, including the ocean, ice cover, and land surfaces. In addition, it aims to deliver estimates of mass anomalies, mass transport, and mass exchange in the Earth system. Surface kinematics and mass transport together are the key to global mass balance determination, and an important contribution to the understanding of the energy budget of our planet.32 –34 Moreover, the system aims to provide the observations that are needed to determine and maintain a terrestrial reference frame of higher accuracy and greater temporal stability than what is available today.35 GGOS as a system (Fig. 4) exploits (and tries to extend) for this purpose the unique constellation of satellite missions relevant to this goal that are in orbit now and those planned for the next two decades, by integrating them into one measurement system. The backbone of this integration is the existing global ground network of tracking stations for the geodetic space techniques, Very Long Baseline Interferometry (VLBI), Satellite Laser Ranging (SLR), Lunar Laser Ranging (LLR), GNSS, and Doppler Orbitography and Radio Positioning Integrated by Satellite (DORIS). GGOS integrates these tracking networks with terrestrial gravity networks. GGOS will complement the space segment and global ground network by airborne and terrestrial campaigns that serve the purpose of calibration and validation, regional densification, and refinement. Assimilation of these observations into models of weather, climate, oceans, hydrology, ice, and solid Earth processes will fundamentally enhance the understanding of the role of surface changes and mass transport in the dynamics of our planet. Furthermore, through the analysis of the dense web of microwave radiation connecting the GNSS satellites with Low Earth Orbiters (LEO) and with the Earth’s surface, a powerful new technique emerges for probing the atmosphere’s composition. GGOS (the observing system) faces two types of scientific and technological challenges, namely an “internal” and an “external” challenge: The “internal” challenge to geodesy is to develop GGOS and the geodetic technologies so that they meet the demanding user requirements in terms of reference frame accuracy and availability, as well as in terms of spatial and temporal resolution and accuracy of the geodetic observations. Developing an observing system capable of measuring variations in the Earth’s shape, gravity field, and rotation with an accuracy and consistency of 0.1–1 ppb, with high spatial and temporal resolution, and increasingly low time latency, is a very demanding task. Accommodating the transition of new

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Fig. 4. Infrastructure contributing to GGOS. The combined infrastructure allows the determination and maintenance of the global geodetic reference frames, and the determination of Earth’s gravity field and rotation. The ground networks and navigation satellites (currently in particular GPS) are crucial for maintaining the reference frame required for high accuracy positioning. In particular, they allow the monitoring of volcanoes, earthquakes, tectonically active regions, and landslide-prone areas. The Low Earth Orbit (LEO) satellites monitor sea level, ice sheets, water storage on land, atmospheric water content, high-resolution surface motion, and variations in the Earth’s gravity field. The latter are caused, to a large extent, by regional and global mass transport in the hydrological cycle.

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technologies as they evolve in parallel to maintaining an operational system is part of this challenge. The “external” challenge is associated with the integration of the “three pillars” into a system providing information on mass transport, surface deformations, and dynamics of the Earth. The Earth system is a complex system with physical, chemical, and biological processes interacting on spatial scales from micrometers to global and temporal scales from seconds to billions of years. Therefore, addressing the “external” challenge requires a “whole Earth” approach harnessing the expertise of all fields of Earth science.

4. GGOS: An Observing System of Layered Infrastructure GGOS as an observing system has five major levels of instrumentation and objects (Fig. 5) that actively perform observations, are passively observed, or both. These levels are: Level 1: the terrestrial geodetic infrastructure including the ground networks of in situ instruments and space-geodetic tracking stations, as well as the data and analysis centers; Level 2: the LEO satellite missions; Level 3: the Middle/Geostationary Earth Orbiters (MEO/GEO) that is, the GNSS and the Lageos-type SLR satellites; Level 4: the planetary missions and geodetic infrastructure on Moon and planets; Level 5: the extragalactic objects. The ground networks and the GNSS are crucial in positioning. Highly accurate orbits for the LEO satellites are determined with the help of ground-based infrastructure as well as GNSS satellites. Level 4 is particularly important for the dynamical reference frame. The stable quasars of Level 5 provide the inertial reference frame fixed in space. These five levels of instrumentation and objects, independent of whether they are active or passive, receivers or emitters or both, are connected by many types of observations in a rather complex way to form the integrated GGOS observing system. In this system, the major observation types at present are: (1) observations of the microwaves emitted by GNSS satellites at the ground and at the LEO satellites; (2) laser ranging to LEOs, dedicated laser ranging satellites, GNSS satellites, and the Moon; (3) microwave observation of extragalactical objects (quasars) by VLBI; (4) instrumentation onboard the LEO satellites measuring accelerations, gravity gradients, satellite orientation, etc.; (5) radar and optical observations of the Earth’s surface (land, ice, glaciers, sea level, etc.) from remote sensing satellite; (6) distance measurements between satellites

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Fig. 5. The five levels of GGOS and their interactions with observations of various types. The infrastructure of GGOS consists of five distinct levels, depending on the distance to the Earth’s surface. See text for details.

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(K-band, optical, interferometry, etc.). In the future, new measurement techniques will evolve and be included into the system. Figure 5 also makes clear that different parts of the overall system are cross-linked through observations and are interdependent. Moreover, all these techniques are affected by the same Earth system processes and they measure the “output” of the same unique Earth system, that is, the various geodetic fingerprints induced by mass redistribution and changes in the system’s dynamics (see Fig. 2). Therefore, consistency of data processing, modeling, and conventions across the techniques and across the “three pillars” is mandatory for maximum exploitation of the full potential of the system. In order to achieve this, GGOS pursues two main roads: (1) The individual parts (i.e. observation types) of the overall system are connected by co-location of different instruments at the same site on the Earth’s surface or on the same satellite or object. This co-location of instruments and sensors is extremely important for the consistency and accuracy of the system and for the integration of the system to perform as one large “instrument.” Moreover, each of the techniques has its own strength and weaknesses, and through co-location, the strengths can be exploited and the weaknesses can be mitigated. (2) The different parts of the Earth system are increasingly linked through consistent and comprehensive models, in order to capture all interactions and feedback between the various components of the Earth system as depicted in Fig. 2, leading eventually to an integrated Earth system model predicting the geodetic fingerprints in the “three pillars” consistently as a basis for improved geodetic analyses. Enhanced modeling will also allow more realistic estimates of the uncertainties of individual observations and products and thus improve the integrability of the different geodetic datasets. GGOS is one of the systems contributing to GEOSS. This requires interoperability of those products and services provided by GGOS to GEOSS with the other systems of GEOSS. While there has been an increasing focus on interoperability between the various IAG Services, achieving interoperability with GEOSS components is still an emerging challenge with the extent not yet fully identified.

5. Conclusions GGOS is IAG’s Observing System. As an organization, it is the main interface for the IAG Services particularly to the major international Earth observation programs, including GEO. As an organization, GGOS

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enhances the visibility of geodesy in general and the work of the IAG Services in particular in society and specifically the relevant scientific organizations and Earth observation system. GGOS links the IAG Services to major user groups. The organization has the main tasks to: (1) identify a comprehensive set of geodetic products and establish the requirements concerning the products’ accuracy, temporal and spatial resolution, latency, and consistency; (2) develop the strategy for GGOS, appropriate to meet these requirements; (3) identify the gaps in the system of IAG Services and develop strategies to close these gaps; (4) ensure the availability, consistency, reliability, accessibility, and interoperability of geodetic observations, products, and models. As an observing system, GGOS builds on the infrastructure of the IAG Services and utilizes signals and observations from relevant infrastructure maintained by governmental authorities and space agencies (e.g. NASA and ESA). Based on this infrastructure, GGOS allows the monitoring of: (1) the deformation of the Earth surface (solid Earth, ocean, and ice) and Earth rotation with submillimeter accuracy; (2) the global gravity field and its time variations with unprecedented accuracy and resolution, particularly through dedicated gravity satellite missions; (3) the water vapor in the troposphere, tropopause height, and electron density in the ionosphere, thus monitoring the atmospheric processes relevant for global warming; (4) many parameters related to natural hazards and disasters, increasingly providing observations relevant for early warning systems. Since GGOS depends to a degree on infrastructure (particularly the satellite missions) provided by others, GGOS has to interact with these providers (particularly the space agencies) in order to ensure continuity of the required infrastructure and to work with them to improve the infrastructure continuously. In order to integrate the infrastructure of the IAG Services into a comprehensive and integrated geodetic Earth observation system of systems with highly accurate and consistent observations and products, GGOS faces main challenges in the combination and integration of all observation techniques into a consistent observing system, and in developing comprehensive modeling of the interactions in the Earth system across the “three pillars.” Already today, geodesy and particularly GGOS has the documented potential to facilitate new insights into the geophysical processes in the Earth system. If the challenges can be met, GGOS will provide a basis for a deeper scientific understanding of the Earth System and the future of our changing planet, as well as an improved basis for Earth observations and geodesy-dependent applications in society at large.

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Acknowledgments The paper has greatly benefited from the work done by many colleagues in the frame of the GGOS 2020 process. The authors would like to thank all those who have contributed to the GGOS 2020 report, particularly the chapter lead authors R. Rummel, D. Sagagian, C. Rizos, J. Zumberge, R. Gross, T. A. Herring, and G. Beutler. The authors are also grateful to two anonymous referees, who provided helpful comments on the original version of the manuscript.

References 1. S. C. Solomon and the Solid Earth Science Working Group, Living on a Restless Planet (NASA, Jet Propulsion Laboratory, Pasadena, California, 2002), also available at http://solidearth.jpl.nasa.gov. 2. B. L. Turner II, W. C. Clark, R. W. Kates, J. F. Richards, J. T. Mathews and W. B. Meyer (eds.), The Earth as Transformed by Human Action: Global and Regional Changes in the Biosphere Over the Past 300 years (University Press, Cambridge, 1990), 713 pp. 3. GEO, Global Earth Observing system of systems GEOSS — 10-year implementation plan reference document — Draft, Tech. Rep. GEO 1000R/ESA SP 1284, ESA Publication Division (ESTEC, Noordwijk, The Netherlands, 2005), avaliable at http://earthobservations.org. 4. B. F. Chao, Eos, Trans. Am. Geophys. Union 84 (2003) 145. 5. H.-P. Plag, National geodetic infrastructure: Current status and future requirements — The example of Norway, Bulletin 112, Nevada Bureau of Mines and Geology, University of Nevada, Reno (2006), 97 pages. 6. R. Rummel, Global integrated geodetic and geodynamic observing system (GIGGOS), in Towards an Integrated Global Geodetic Observing System, eds. R. Rummel, H. Drewes, W. Bosch and H. Hornik, International Association of Geodesy Symposia, Vol. 120 (Springer, Berlin, 2000). 7. K. H. Ilk, J. Flury, R. Rummel, P. Schwintzer, W. Bosch, C. Haas, J. Schr¨ oter, D. Stammer, W. Zahel, H. Miller, R. Dietrich, P. Huybrechts, H. Schmeling, D. Wolf, H. J. G¨ otze, J. Riegger, A. Bardossy, A. G¨ unter and T. Gruber, Mass transport and mass distribution in the Earth system, Tech. Rep., GOCE-Projectb¨ uro Deutschland, Technische Universit¨ at M¨ unchen, GeoForschungsZentrum Potsdam (2005). 8. C. Kreemer and W. E. Holt, Geophys. Res. Lett. 28 (2001) 4407. 9. H. P. Kierulf, L. Bockmann, O. Kristiansen and H.-P. Plag, Foot-print of the space-geodetic observatory, Ny-˚ Alesund, Svalbard, Proc. Second IVS General Meeting, 4–6 February 2002, Tsukuba, Japan, eds. N. Vandenberg and K. Baver (NASA Goddard Space Flight Center, Greenbelt, MD). 10. C. Kreemer, W. E. Holt and A. J. Haines, Geophys. J. Int. 154 (2003) 8.

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11. G. Blewitt, D. Lavall´ee, P. Clarke and K. Nurutdinov, Science 294 (2001) 2342. 12. D. Dong, P. Fang, Y. Bock, M. K. Cheng and S. Miyazaki, J. Geophys. Res. 107 (2002) 2075, doi: 10.1029/2001JB000573. 13. X. Wu, M. B. Heflin, E. R. Ivins, D. F. Argus and F. H. Webb, Geophys. Res. Lett. 30 (2003) 1742, doi: 10.1029/2003GL017546. 14. R. S. Gross, G. Blewitt, P. J. Clarke and D. Lavall´ee, Geophys. Res. Lett. 31 (2004), doi: 10.1029/2004GL019589. 15. Z. Lu, C. Wicks, D. Dzurisin, W. Thatcher, J. Freymueller, S. McNutt and D. Mann, Geophys. Res. Lett. 27 (2000) 1567. 16. R. Lanari, G. De Natale, P. Berardino, E. Sansosti, G. P. Ricciardi, S. Borgstrom, P. Capuano, F. Pingue and C. Troise, Geophys. Res. Lett. 29 (2002), doi: 10.1029/2001GL014571. 17. A. Bonforte and G. Puglisi, J. Geophys. Res. 108 (2003) 2153, doi: 10.1029/2002JB001845. 18. A. Ferretti, F. Novali, R. B¨ urgmann, G. Hilley and C. Prati, Eos, Trans. Am. Geophys. Union 85 (2004) 317, 324. 19. P. Banerjee, F. F. Pollitz and R. B¨ urgmann, Science 308 (2005) 1769. 20. C. Vigny, W. J. F. Simons, S. Abu, R. Bamphenyu, C. Satirapod, N. Choosakul, C. Subarya, A. Socquet, K. Omar, H. Z. Abidin and B. A. C. Ambrosius, Nature 436 (2005) 201. 21. C. Kreemer, G. Blewitt, W. C. Hammond and H.-P. Plag, Earth Planets Space 58 (2006) 141. 22. T. Strozzi, L. Tosi, U. Wegm¨ uller, P. Teatini, L. Carbognin and R. Rosselli, Geophys. Res. Lett. 29 (2002) 345. 23. G. Blewitt, C. Kreemer, W. Hammond, H.-P. Plag, S. Stein and E. Okal, Geophys. Res. Lett. 33 (2006) L11309, doi: 10.1029/2006GL026145. 24. D. Jerrett and J. Nash, Phys. Chem. Earth 26 (2001) 457. 25. G. Elgered, H.-P. Plag, H. Marel, S. Barlag and J. Nash, COST Action 716 Exploitation of Ground-based GPS for Operational Numerical Weather Prediction and Climate Applications, COST European cooperation in the field of scientific and technical research, no. EUR 21639 in COST European cooperation in the field of scientific and technical research (European Commission, 2005). 26. P. Poli, P. Moll, F. Rabier, G. Desroziers, B. Chapnik, L. Berre, S. B. Healy, E. Andersson and F.-Z. El Guelai, J. Geophys. Res. 112 (2007) D06114, doi: 10.1029/2006JD007430. 27. H.-P. Plag, GGOS and it user requirements, linkage and outreach, in Dynamic Planet — Monitoring and Understanding a Dynamic Planet with Geodetic and Oceanographic Tools, eds. P. Tregoning and C. Rizos, International Association of Geodesy Symposia, Vol. 130 (Springer Verlag, Berlin, 2006). 28. R. Rummel, H. Drewes, W. Bosch and H. Hornik (eds.), Towards an Integrated Global Geodetic Observing System, International Association of Geodesy Symposia, Vol. 120 (Springer, Berlin, 2000).

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29. GEO, The Global Earth Observing System of Systems (GEOSS) — 10-Year Implementation Plan, available at http://earthobservations.org (2005). 30. H.-P. Plag and M. Pearlman (eds.), The Global Geodetic Observing System: Meeting the Requirements of a Global Society on a Changing Planet in 2020 — The Reference Document (Global Geodetic Observing System, 2007), available at http://geodesy.unr.edu/ggos/ggos2020/. 31. H.-P. Plag, G. Beutler, R. Forsberg, C. Ma, R. Neilan, M. Pearlman, B. Richter and S. Zerbini, Linking the Global Geodetic Observing System (GGOS) to the Integrated Global Observing Strategy Partnership (IGOS-P) through the Theme “Earth System Dynamics”, in Dynamic Planet — Monitoring and Understanding a Dynamic Planet with Geodetic and Oceanographic Tools, eds. P. Tregoning and C. Rizos, International Association of Geodesy Symposia, Vol. 130 (Springer Verlag, Berlin, 2006). 32. R. Rummel, H. Drewes and G. Beutler, Integrated Global Geodetic Observing System (IGGOS): A candidate IAG project, in Vistas for Geodesy in the New Millennium, eds. J. Adam and K.-P. Schwarx, International Association of Geodesy Symposia, Vol. 125 (Springer, Berlin, 2002). 33. R. Rummel, M. Rothacher and G. Beutler, J. Geodynamics 40 (2005) 357. 34. H. Drewes, The science rationale of the Global Geodetic Observing System GGOS, in Dynamic Planet — Monitoring and Understanding a Dynamic Planet with Geodetic and Oceanographic Tools, eds. P. Tregoning and C. Rizos, International Association of Geodesy Symposia, Vol. 130 (Springer Verlag, Berlin, 2006). 35. G. Beutler, H. Drewes, H.-P. Plag, C. Reigber, M. Rothacher and R. Rummel, IAG GGOS Implementation Plan, Tech. Rep., GeoForschungZentrum Potsdam, Germany (2005).

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Advances in Geosciences Vol. 13: Solid Earth (2007) Ed. Kenji Satake c World Scientific Publishing Company


THE INTERNATIONAL LASER RANGING SERVICE MICHAEL PEARLMAN Harvard-Smithsonian Center for Astrophysics 60 Garden St., Cambridge, MA 02138, USA CAREY NOLL NASA Goddard Space Flight Center, Code 690 Greenbelt, MD 20771, USA [email protected] JAN MCGARRY NASA Goddard Space Flight Center, Code 694 Greenbelt, MD 20771, USA WERNER GURTNER Astronomical Institute of Bern, Sidlerstrasse 5 CH-3012 Berne, Switzerland ERRICOS PAVLIS Joint Center for Earth Systems Technology University of Maryland, Baltimore County 1000 Hilltop Circle Baltimore, MD 21250, USA

The International Laser Ranging Service (ILRS) was established in September 1998 as a service within the IAG to support programs in geodetic, geophysical, and lunar research activities and to provide data products to the International Earth Rotation and Reference Frame Service (IERS) in support of its prime objectives. The ILRS develops the standards and specifications necessary for product consistency and the priorities and tracking strategies required to maximize network efficiency. This network consists of more than 30 Satellite Laser Ranging (SLR) stations, routinely tracking nearly 30 retroreflectorequipped satellites and the Moon in support of user needs. The Service collects, merges, analyzes, archives, and distributes satellite and lunar laser ranging data to satisfy a variety of scientific, engineering, and operational needs and encourages the application of new technologies to enhance the quality, quantity, and cost effectiveness of its data products. The ILRS works with the global network to improve station performance, new satellite missions in the design and building of retroreflector targets to maximize data quality and quantity, and science programs to optimize scientific data yield. The ILRS Central Bureau maintains a comprehensive web site (http://ilrs.gsfc.nasa.gov) as the primary vehicle for the distribution of information within the ILRS community. 129

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During the last few years, the ILRS has addressed very important challenges. Several new SLR stations have enhanced the global coverage of the network, particularly in the Southern Hemisphere. Two Combination Centers have been established to provide Official ILRS Data Products to the IERS for the process of maintaining the ITRF (International Terrestrial Reference Frame), and seven ILRS Analysis Centers have been established to provide the solutions for the combination process. Procedures have been introduced to track vulnerable satellites, such as ICESat and ALOS. Work continues at several institutions on the ground system development toward the next generation laser ranging system with the introduction of kilohertz pulse repetition rates, greater automation, and greater eye safety. Work also continues on the development of new lighter weight retroreflector arrays.

1. ILRS Organization The ILRS coordinates the activities of the international laser ranging community from data acquisition through the delivery of products to the users. The ILRS is organized into Tracking Stations and Subnetworks, Operations Centers, Data Centers, Analysis and Associate Analysis Centers, a Central Bureau, Working Groups, and a Governing Board (see Fig. 1, Refs. 1 and 2).

Fig. 1.

ILRS organization.

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ILRS Tracking Network (as of July 2007). Fig. 2.

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Stations in the ILRS Tracking Network range to the approved constellation of artificial satellites and the Moon with state-of-the-art laser ranging systems and transmit their data in near-real time to the ILRS Data Centers. The full network currently consists of more than 30 SLR stations, as shown in Fig. 2. For the most part, stations are designed by the local participating agencies using fairly standard components. Some examples of stations are shown in Fig. 3. The majority of ILRS stations have a co-located GNSS receiver that adheres to standards established by the International GNSS Service (IGS). Some are also co-located with VLBI, DORIS, and absolute gravity instrumentation. The ILRS has given strong encouragement to the development of Fundamental Reference Stations, where combination of several space geodetic techniques including SLR, VLBI, GNSS, DORIS,

Fig. 3. Selected ILRS network stations (clockwise from top left, Zimmerwald Switzerland; Greenbelt, USA; Maui, USA; Matera, Italy).

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and absolute gravimetry are co-located to strengthen the reference system constraints and system synergy. The current network is very far from an “optimal” one and there are currently studies underway to design an improved “space geodetic” network of fiducial observatories where SLR and all other primary space techniques will be present. The void over most of Africa and Asia has major impact on the quality of the orbits determined with SLR and by implication, the geodetic products based on them, be it daily resolution EOP, gravitational variations, tidal parameters, etc. This lack of sites over vast expanses has also severe repercussions on the definition of the ITRF, with the definition of the origin and scale being the most important. A similar situation rises in the Pacific area where in addition to the limited availability of SLR systems, we also face the lack of suitable landmass to locate any available systems. Yet, this area is very important in terms of providing ground control for precise orbits, since it is the largest oceanic area over which the oceanographic missions carrying radar altimeters require very high accuracy orbits for the study of secular changes in the mean sea level. The oceanographically complicated area surrounding Indonesia and the northern coast of Australia would benefit tremendously from the future addition of an SLR system somewhere in the neighborhood of Darwin. ILRS Operations Centers collect and merge the data from the tracking sites, provide initial quality checks on these incoming data, reformat and compress the data if necessary, and relay the data to an ILRS Data Center. Two Global Data Centers archive all the ranging data and auxiliary data (e.g. station log files and satellite orbit predictions), make the data available to the ILRS Analysis Centers and external users of the data, and act as distribution centers for the primary ILRS products. The Analysis and Associate Analysis Centers routinely generate the official ILRS products (station coordinates at one-week intervals, Earth Orientation Parameters at one-day intervals) as well as special products, such as satellite predictions, time-bias information, precise orbits for special-purpose satellites, or scientific data products of a mission-specific nature. Lunar Analysis Centers process ranging data to the targets on the Moon and produce lunar-specific data products. There are currently about 30 ILRS Analysis and Associate Centers. The Central Bureau (CB) is responsible for the daily coordination and management of the ILRS. The CB maintains the ILRS web site, a

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source for all SLR- and LLR-related information including details about the organization and operation of ILRS, and also an entry point to the data and products stored at the Data Centers. The CB maintains the ILRS documentation, organizes meetings and workshops, and issues service reports. The ILRS has four standing and one ad-hoc Working Groups that provide the expertise necessary to make technical decisions, to plan programmatic courses of action and are responsible for reviewing and approving the content of technical and scientific databases maintained by the Central Bureau (see Fig. 1). The ILRS Governing Board (GB) is responsible for the general direction of the service and defines official ILRS policy and products, determines satellite-tracking priorities, and develops standards and procedures. The 16member body interacts with other services and organizations and is selected from ILRS associates representing all components of the service.

2. Scientific Applications of Laser Ranging Laser ranging measurements provide a long-term stable time history of station positions and precision orbit determination.3 Data products support maintenance of the Terrestrial Reference Frame (Earth center of mass and scale), plate tectonics and crustal deformation, static and time-varying gravity field, Earth Orientation (polar motion and length of day), precision orbit determination and calibration of altimetry missions, mass distribution studies, relativity, and lunar and space science.

2.1. The global terrestrial reference frame The terrestrial reference system is the basis through which we connect and compare measurements over space, time, and evolving technologies. It is the means by which we know that measured change over time is real and not corrupted with instabilities in our measurement system. One of the best-known scientific realizations of a global terrestrial reference system is the ITRF,4 updated every few years by the IERS. It is based on contributions from the four different space geodetic techniques (SLR, VLBI, GNSS, and DORIS) consisting of solutions for the positions and velocities of all participating tracking stations in an Earth-fixed geocentric coordinate system. The most important contributions of the

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laser ranging technique to the reference frame are the fixing of its origin (defined with respect to the center-of-mass of the Earth, including oceans and atmosphere) and its scale (defined by the speed of light, realized mainly through the measurement of the time of propagation, i.e. the ranges to satellites).5 Origin and scale are crucial elements, not only for “classical” referencing purposes (i.e. crustal deformation studies), but also as providers of an absolute reference for investigations on sea level change, ice budget, etc. ILRS contributions come either as multi-year solutions based on ranges to the geodynamic satellites LAGEOS-1 and -2 or, within the current IERS Combination Pilot Project, as time series of weekly solutions.

2.2. The Earth in space The connection between the terrestrial and the celestial reference systems is given by the current position of the Earth’s body with respect to its current axis of rotation (polar motion), its angular rotation about its axis (UT1), or the temporal change of this angle (angular velocity, length-of-day), and the orientation of the axis of rotation in space (precession and nutation). One of the official ILRS products is the time series of polar motion and length-of-day, submitted weekly to the IERS for combination with the time series generated by the other space geodetic techniques.6,7

2.3. The Earth’s gravity field Global gravity field models are based on a combination of spaceborne and ground-based measurements.8 Dedicated gravity satellite missions including satellite-to-satellite tracking in-orbit observation of derivatives of the gravity field and ocean-surface altimetry have provided high spatial and temporal resolution of the gravity field. The lower-degree terms of this field and their secular changes (e.g. of the dynamic flattening of the Earth) are most accurately observed by laser ranging. In this way, SLR provides extremely valuable information on (changes in) the overall mass distribution of Earth.9,10

2.4. Precise orbit determination and verification SLR provides routine precise orbit determination for some missions and verification and calibration of precise orbits determined with other tracking

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techniques such as GNSS or DORIS for others.11 The high accuracy and unambiguous nature of SLR data makes it an independent source of quality control and calibration for other tracking techniques. In particular, SLR has been used in all of the recent ocean and ice topography missions to support altimeter measurements and for a number of special engineering missions (e.g. altimeter calibration).12 SLR, with its totally passive spaceborne reflectors, also acts as a backup for active tracking techniques. It has saved satellite missions (ERS-1, GFO-1, TOPEX/Poseidon, and Meteor-3M) after the failure of the primary tracking system. The ILRS continues to encourage new missions with high precision orbit requirements to include retroreflectors as a fail-safe backup tracking system, to improve or strengthen overall orbit precision, and to provide important intercomparison and calibration data with onboard microwave navigation systems.

2.5. Lunar ranging The two ILRS stations currently capable of routinely tracking the four lunar targets have a long history of providing LLR data: the McDonald Observatory in Texas has been in operation since the Apollo 12 mission (since 1985 with the current system), whereas the Grasse Observatory in France started lunar laser ranging in 1987. Several stations have demonstrated lunar capability while others have tracked the Moon for some periods of time (e.g. Maui, Hawaii, 1984–1990). A number of stations (e.g. Matera, Italy and Mount Stromlo, Australia) are planning to include lunar tracking in their future activities. Applications in gravitational physics include: testing of the Equivalence Principle; (limits for) timevariation of the gravity constant G; and the assessment of the geodetic precession.13 Applications in lunar science include the determination or the improvement of lunar ephemerides and rotation; dissipation-caused (negative) acceleration; and an assessment of the interior and the Lunar Love numbers.14

2.6. Role within other activities In early 2004, under its new reorganization, the International Association of Geodesy (IAG) established the Global Geodetic Observatory System

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(GGOS) project to coordinate geodetic research in support of scientific and applications disciplines. GGOS is intended to integrate different geodetic techniques, models and approaches to provide better consistency, long-term reliability, and understanding of geodetic, geodynamic, and global change processes. Through the IAG’s measurement services (IGS, IVS, ILRS, IDS, and IGFS), GGOS will work to ensure the robustness of the three aspects of geodesy: geometry and kinematics, Earth orientation and rotation, and static and time-varying gravity field. GGOS will identify geodetic products and establish requirements on accuracy, time resolution, and consistency. The project will work to coordinate an integrated global geodetic network and implement compatible standards, models, and parameters. A fundamental aspect of GGOS is the establishment of a global network of stations with co-located techniques, working together to deliver the most accurate and robust reference frame. The ILRS is one of the service participants in GGOS, bringing its unique strengths to the geodetic complex.

3. Satellites Tracked by ILRS Since its inception, SLR has tracked more than 50 satellites with retroreflectors. Currently, the ILRS tracks 25 satellites for geodynamics, remote sensing (altimeter, SAR, etc.), gravity field determination, general relativity, validation of global navigation systems satellite orbits, and engineering tests (see Fig. 4). Altitudes range from a few hundreds of kilometers to geosynchronous altitudes (36,000 km). Two stations, Grasse, France and McDonald, USA, routinely range to four targets on the Moon. Satellites are added and deleted from the ILRS tracking roster as the GB approves new observation campaigns and older ones are completed. The ILRS assigns satellite-tracking priorities in an attempt to maximize data yield on the full satellite complex while at the same time placing greatest emphasis on the most immediate data needs. Nominally, tracking priorities decrease with increasing orbital altitude and increasing orbital inclination (at a given altitude). Priorities of some satellites are then increased to intensify support for active missions (such as altimetry), special campaigns, and post-launch intensive tracking campaigns. New missions added during the past year are ALOS, ANDE-RR, TerraSAR-X, GLONASS-102, ETS-8, and GIOVE-A. Missions scheduled for ILRS tracking support over the next year include GIOVE-B, PROBA-2,

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Examples of satellites tracked by the ILRS network: LAGEOS-1, Jason-1, and GIOVE-A (photo courtesy of ESA).

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Schematic of Reflector satellite.

Jason-2, LRO-LR, and so on. It is anticipated that the entire 30-satellite Galileo complex, to be launched by 2013 will require, at least intermittent, SLR tracking. The ILRS supports space engineering studies on some rather unique missions. The Russian Reflector satellite included retroreflectors over its nearly 1 12 m length (Fig. 5). Differences in the laser return time-of-arrival were used to interpret the orientation and dynamics of the satellite (Fig. 6). Another mission, the Naval Research Laboratory’s Tether Physics and Survivability satellite (TiPS) with retroreflector arrays on two satellites separated by a four-kilometer tether was tracked by SLR to study tether dynamics in space.

4. Network Performance Satellite Laser Ranging uses short pulse lasers and fast timing electronics to measure the two-way range from a ground station to retroreflector

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Fig. 6. Range residual pattern on the Reflector satellite observed at Yarragadee (courtesy of N. Parkhomenko, RSA).

arrays on satellites and the Moon (Fig. 7). The measurement is the roundtrip travel time corrected for optical refraction and spacecraft centerof-mass. The prime data product from the ILRS stations are normal points, which are full-rate ranging data averaged over time intervals from 15 s to 5 min depending upon the satellite altitude. Absolute accuracy is typically subcentimeter. Data are archived and available to the user on a pass-by-pass basis. The space segment is totally passive, and the refraction model in the optical wavelength region is relatively simple. Laser ranging stations operate during both day and night time, and the data are available to the user in near-real-time through the data centers. Laser ranging provides centimeter orbital accuracy and brings a unique ability to detect small changes through the analysis of long-term stable time series of data. Most of the current laser tracking stations range 10 times per second during part or all of the satellite pass, with many stations interleaving passes from different satellites (see Fig. 8). An example of a productive day at Yarragadee is shown in Fig. 9.

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Fig. 7.

Fig. 8.

Laser ranging configuration.

Interleaved passes from Zimmerwald Switzerland station.

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An example of a productive day of tracking at Yarragadee Australia. Fig. 9.

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5. Analysis and Data Products The most important aspects of the SLR/LLR observations are absolute accuracy, and long, stable time histories. Accuracy approaches the level of a few mm for modern stations; time histories can be 25–30 years for a few core sites, or even longer for the high-altitude satellites (e.g. LAGEOS). Since its inception, the ILRS put the generation of official analysis products high on its agenda. The Analysis Working Group has initiated several pilot projects, to address questions on the official products, to define standards, to reach consensus on product definition, and ultimately, to arrive at reliable and high-quality operational products.

5.1. “Positioning and EOP” product The first-ever Pilot Project focused on the computation of the best-possible ILRS product for station coordinates and Earth Orientation Parameters (EOPs). Various scenarios were defined and tested for establishing the proper satellite mix, means of representing the results, computational strategies, etc. This resulted in the currently operational processing scheme. At this moment, seven different Analysis Centers (ACs), (ASI/Italy, BKG/ Germany, DGFI/Germany, GA/Australia, GFZ/Germany, JCET/USA, and NSGF/UK) deliver weekly solutions on LAGEOS-1 and -2 and Etalon-1 and -2 for global station coordinates and EOPs on the Tuesday of each week. These solutions are merged into a combined solution by the two official Combination Centers (CCs) at ASI and DGFI. Based on the contributions that the analysis groups made to the Pilot Project and on the evaluation of the quality of their results, the ILRS adopted ASI as its official CC, with prime responsibility for the generation of combination solutions for external customers such as IERS. DGFI is the official Backup ILRS CC with the same product generation schedule. Several other institutions such as GRGS/OCA, France, and NCL/UK are also well on their way in the development of their contributions to the official ILRS product. Once they pass a standardized set of benchmarks in the near future, these groups become additional ACs. As an illustration of the quality of the individual solutions as well as that of the official combination product (the quality of the primary and the backup solutions is effectively identical), Table 1 gives a summary of the scatter of weekly solutions for the Cartesian component offsets and

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Table 1. Scatter of similarity transformation parameters w.r.t. ITRF2000 for successive weekly ILRS solutions for 2006 (offsets in mm, scale in ppb).

Individual ASI BKG DGFI GFZ JCET NSGF Combination ILRS-A

Tx

Ty

Tz

Scale

3.8 4.0 4.7 4.2 3.0 6.1

3.1 1.6 3.9 2.7 2.2 7.3

8.5 2.4 9.0 6.9 7.1 12.0

1.2 0.6 0.8 0.9 0.9 1.4

2.8

2.2

6.5

0.5

global scale from a similarity transformation with respect to ITRF2000. The combination solution gives naturally the best result; however, all of these products indicate the high quality that ILRS ACs and CCs provide. The combination solutions are used for a variety of purposes: the IERS Combination Pilot Project, the IERS/NEOS Bulletin A, etc. At the request of IERS, the ILRS AWG initiated a reprocessing of the older SLR data using the exact standards as currently adopted for the operational product. These products will be finalized by the end of 2007, and they will serve as inputs for a successor to ITRF2005 and other applications. At this moment, the reprocessing of older data focuses on the 1983–2007 timeframe, and for SLR measurements on LAGEOS-1 and -2, augmented with Etalon-1 and -2 data obtained in the years following 2001. The temporal variations of the weekly solution origin offsets from the long-term mean location realized by ITRF2000 (approximating the “geocenter”) can be seen in Fig. 10. Note the dominant annual signal in the z-component, clearly a manifestation of mass redistribution in the Earth system, related to climate. Similarly, Fig. 11 shows the temporal variation of the scale of the weekly solutions with respect to that of ITRF2000.

5.2. “Benchmarking” process A Benchmarking Pilot Project has been established early on, to provide internal quality checks and quality control over the analysis process. Initially, this was used to scrutinize individual elements of the SLR observations, measurement corrections, and parameter solutions. Having

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ILRS-A vs. ITRF2000 From ILRS-A 100

ORIGIN OFFSET Tx, Ty, Tz [mm]

Mean [mm] Std. Dev.[mm]

Tx -0.75 5.05

Ty -0.30 5.12

Tz -7.47 10.70

Tx Ty Tz

50

0

-50

 2006 Copyright JCET, UMBC.

-100 94/01

96/01

98/01

08/01/14

00/01 Date

02/01

04/01

06/01

08/01

Fig. 10. Time series of the origin offsets after a similarity transformation of the weekly ILRS-A product with respect to ITRF2000 for 1993–2006.

ILRS-B vs. ITRF2000 From ILRS-B 10

Mean [ppb] Std. Dev.[ppb]

-0.14 0.68

SCALE

SCALE [ppb]

5

0

-5

 2006 Copyright JCET, UMBC.

-10 94/01 08/01/10

96/01

98/01

00/01 Date

02/01

04/01

06/01

08/01

Fig. 11. Time series of the scale differences after a similarity transformation of the weekly ILRSB product with respect to ITRF2000 for 1993–2006.

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reached a fully operational status, the Benchmarking Process is now an integral part of the AWG procedures used to assess the quality of new candidate contributors to the ILRS combination products and to identify possible errors.

5.3. New products In addition to the Station Coordinate and EOP Product, the AWG is currently working on the delivery of additional official products including satellite orbits and a time series of temporal variations in the origin of the ITRF with respect to the origin of the weekly solution,15 the latter being the closest approximation to the “geocenter.” These products are expected to become routinely available by the end of 2007.

6. Modeling The precision of the travel-time measurement is now of the order of several tens of picoseconds, corresponding to a few millimeters in distance. Ranging accuracy is limited mainly by errors in modeling for refraction propagation and the extrapolation from the reflectors to the satellite center-of-mass.

6.1. Refraction Since the launch of LAGEOS-1 in 1976, laser ranging stations have used the Marini and Murray model16 for atmospheric propagation correction. The model works well at higher elevations, but degrades substantially below 20◦ , and ILRS recently (2002) “lowered” the minimum elevation cut-off to 10◦ above the horizon to improve the decorrelation of measurement biases and station height. A new model available since 200417 provides improved refraction correction at lower elevations, and it has been adopted by ILRS and the IERS as the new standard, along with the companion mapping functions that work for all wavelengths used in SLR today.18 Additional improvements are expected when ILRS implements modeling of horizontal refraction gradients due to circulation in the lower atmosphere. Experiments with 2 years of SLR data have shown a ∼20%–30% reduction in the residual variance when accounting for these errors.19

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6.2. Retroreflector array Early retroreflector designs, even those of the LAGEOS era, relied on multi-cornercube returns to maximize return signal strength. Determining the effective reflection plane with respect to the satellite center-of-mass is a fundamental correction in SLR metrology. This correction is highly dependent upon return signal strength and the ranging station detector configuration, neither of which were sufficiently well considered in the early correction models. Errors could be as high as a centimeter, when the entire error budget of a normal point aside from this correction is at or below the millimeter for most sites. With SLR being the principal technique to establish unambiguously the scale of the ITRF, an error of that size amounts to almost 2 ppb, when we are striving for 0.1–0.2 ppb for the future ITRF realizations. This correction has proven to be one of the more significant contributors in measurement biases today and there are coordinated efforts such as theoretical studies20 and calibration measurements,21 addressing this error source under the aegis of ILRS. Models are now being implemented that use ground system characteristics to improve the modeled correction to the level better than a single millimeter.

7. Advances Underway A number of advances are currently being implemented that will substantially improve data productivity and quality, while at the same time reduce operational costs. Many of these advances that are now working in one or two stations are envisioned as general characteristics of the future SLR network.

7.1. Automation Stations are implementing increased automation to reduce personnel costs and facilitate data throughput. The new Mount Stromlo Australia station was designed from the beginning with around-the-clock fully automated, unmanned operations. The Zimmerwald laser station operates autonomously for periods of several hours each day. The fully automated NASA Next Generation SLR (NGSLR) system is currently under development.

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7.2. KHz ranging With higher-repetition-rate lasers, faster event timers, and better control software, SLR systems are now able to significantly improve the ranging signal-to-noise conditions, while maintaining modest laser characteristics. The new Graz SLR station was the first to successfully operate a 2 kHz laser system, increasing the data volume by up to two orders of magnitude.22 The NGSLR prototype is also being developed with this capability, as are upgrades at several other stations. 7.3. New, more powerful stations A number of new systems with large meter-class telescopes and state-of-theart optical and mechanical performance are now operational. This helps to give the tracking network a mix of capabilities to better match the range of targets that now appear on the ILRS roster. The Matera station in southern Italy and the remotely controlled Tanegashima station in Japan both use powerful lasers and large optics to achieve single-shot range precision of a few millimeters. 7.4. Two-color ranging Several groups are using two-wavelength ranging which provides a promising technique for developing better models for the refraction delay imposed by the atmosphere. Two-color ranging at 423 nm and 846 nm has been underway experimentally at Concepci´ on, Chile for some time. The Zimmerwald station began routine operations at these same wavelengths in 2002. Other stations (including GSFC and Matera) have also demonstrated dual-wavelength capability, some of them with superior accuracy using streak cameras. Key to the success of this approach for refraction modeling is the extreme accuracy in measuring the differential time-of-flight of the two pulses at different wavelengths. Present technology does not permit us to utilize this approach now; however, there are major advances in developing the required technology, so we are looking forward to a promising future for the improvement of the refraction models, and hence also the ILRS science products. 7.5. Improved satellite retroreflector array design Multi-cornercube arrays will spread the return pulse. Even with LAGEOS, the return signal is smeared over several centimeters, making measurements

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highly dependent upon signal strength and ground system properties. Most of the recently launched satellites are using standardized arrays with restricted cornercube view. The spherical satellite GFZ-1, Westpac, and LARETS experimented with special reflector geometries to limit access to a very few (or single) cubes. Using another approach, the Russian Space Agency has provided the ILRS with a spaceborne test Luneberg Sphere that gives the same array correction for a wide variety of aspect angles.23 An improved version is now being prepared for launch in a sunsynchronous low Earth orbit in 2008. Efforts are also underway to improve the efficiency of the arrays for high-orbiting spacecraft to increase the effective cross-section while reducing the payload weight and surface area, both of which are at a premium on the spacecraft. The recently launched ETS-8 synchronous satellite is using a newer design array with uncoated cubes (totally internally reflecting) with considerable success. Work is also underway on hollow cube options being proposed for possible arrays for the GPS III series of satellites (Fig. 12). Commercial cubes will undergo performance testing in late 2007 at NASA’s Goddard Space Flight Center and at the Laboratori Nazionali di Frascati (LNF) in Italy in a newly built environmental test chamber.21

7.6. Transponders Optical transponders for extraterrestrial ranging are currently under early development by several research teams. An optical transponder is a combination of a laser-ranging receiver and a separate laser pulse transmitter. As opposed to two-way ranging with retroreflector targets,

Fig. 12. Single hollow cube and hollow cube array configuration (drawing courtesy of Armando Morell, NASA Goddard Space Flight Center).

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one-way ranging with a transponder would offer the exciting opportunity of ranging to Mars, planetary moons or orbiters, and deep space missions.24 These transponders will also help to connect the terrestrial reference frame with reference systems used for planetary missions.

7.7. New missions The Ocean Surface Topography Mission (OSTM) will continue the oceanography program begun by the TOPEX/Poseidon and Jason-1 missions. An updated version of the Jason-1 satellite (Jason-2) scheduled launched in June 2008, will use an altimeter and a microwave radiometer to monitor global ocean circulation, study the tie between the oceans and atmosphere, improve global climate predictions, and monitor events such as El Nino conditions and ocean eddies. Jason-2 will also carry the Time Transfer by Laser Link (T2L2) time-transfer experiment. SLR will be used for orbit determination and instrument validation. The Galileo radio navigation system will ultimately have 30 satellites in orbit by 2013. The current mission plan is to have retroreflector arrays on all of the satellites for precision orbit determination and validation of the radio system. The first system test satellite GIOVE-A is currently in orbit; the second, GIOVE-B, is scheduled for launch in April 2008. The role of these test satellites is to secure the Galileo frequency allocations by providing a signal in space, characterize the on-board clock, and test some of the on-board systems. PROBA-2, the second in a series of technology demonstration missions by the European Space Agency is scheduled for launch in December 2007. The series is intended to provide in-orbit demonstration and evaluation of new hardware and software spacecraft technologies, on-board system operational autonomy, and in particular, Earth observation and space environment instruments. PROBA-B will carry solar observation and plasma measuring instruments in addition to new spacecraft components for testing. GPS and SLR will provide orbit determination. The Lunar Reconnaissance Orbiter (LRO), scheduled for launch in October 2008, will orbit the Moon to measure the ground topography with laser altimetry and to study other lunar characteristics such as gravity field, radiation conditions, lighting characteristics, and surface properties in preparation for future human landing. Laser ranging will be used to improve upon the radio tracking accuracy available with C-band. Laser ranging from the Earth will make one-way range measurements to a receiver

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One version of the LARES satellite design.

on the satellite; recorded time on the satellite clock will be transmitted to Earth by telemetry for decoding into range. The need for new, well-designed geodetic SLR targets has always been advocated by ILRS. In response to that need, a small group of Italian, US, and German scientists proposed the design and launch of a “mini-LAGEOS” satellite named LARES (after LAser RElativity Satellite).25 The primary goal for this mission is to improve the accuracy of the space-geodetic general relativity test for frame-dragging.7 Although at a lower orbit than LAGEOS, the new design (Fig. 13) aims to improve orbital accuracy with a significantly smaller area-to-mass ratio, better optical, mechanical, and thermal properties, and careful calibration and testing prior to the launch (∼2009).

8. Conclusions Laser ranging has proven to be a fundamental component of the spacegeodetic complex, offering a straightforward, conceptually simple, highly accurate, and unambiguous observable. It provides essential contributions to geosciences, space sciences, and fundamental physics. It will play an important role in the GGOS project. Current and future challenges lie in the improvement of the accuracy, reliability, and availability of the data, and in the long-term support of the network. Many of the technological building blocks for the next generation

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of laser ranging have already been demonstrated. Their comprehensive implementation will bring dramatic improvements to the capability of the technique.

Acknowledgments The authors would like to acknowledge the support of the International Laser Ranging Service and its contributing organizations.

References 1. M. Pearlman, J. Degnan and J. Bosworth, Adv. Space Res. 30(2) (2002) 135–143. 2. W. Gurtner, R. Noomen and M. Pearlman, Adv. Space Res. 36(3) (2005) 327–332. 3. M. Watkins and R. Eanes, Adv. Space Res. 13(11) (1993) 251–255. 4. Z. Altamimi, X. Collilieux, J. Legrand, B. Garayt and C. Boucher, J. Geophys. Res. 112 (2007) B09401, doi: 10.1029/2007JB004949. 5. M. Watkins and R. Eanes, Geophys. Res. Lett. 24(17) (1997) 2231–2234. 6. M. K. Cheng and B. D. Tapley, J. Geophys. Res. 109 (2004) B09402, doi: 10.1029/2004JB003028. 7. I. Ciufolini and E. C. Pavlis, Nature 431 (2004) 958–960. 8. J. Ries, R. Eanes, C. K. Shum and M. Watkins, Geophys. Res. Lett. 19(6) (1992) 529–531. 9. R. Ray, R. Eanes and B. Chao, Nature 381 (1996) 595–597. 10. B. Chao and R. Eanes, Geophys. J. Int. 122 (1995) 755–764. 11. C. Urschl, G. Beutler, W. Gurtner, U. Hugentobler and S. Schaer, Adv. Space Res. 39(10) (2007) 1515–1523. 12. G. Metris, D. Vokrouhlicky, J. Ries and R. Eanes, J. Geophys. Res. 102(B2) (1997) 2711–2729. 13. J. G. Williams, S. G. Turyshev and D. H. Boggs, Phys. Rev. Lett. 93 (2004) 261101, doi: 10.1103/PhysRevLett.93.261101. 14. J. G. Williams, S. G. Turyshev, D. H. Boggs and J. T. Ratcliff, Adv. Space Res. 37(1) (2006) 67–71. 15. E. C. Pavlis, Vistas for geodesy in the new millennium, Proc. 2001 IAG, eds. J. Adam and K.-P. Schwarz (Springer-Verlag, NY, 2002), pp. 36–41. 16. J. Marini and C. Murray, GSFC Report X-591-73-351 (1973). 17. V. B. Mendes and E. C. Pavlis, Geophys. Res. Lett. 31(14) (2004) L14602. 18. V. B. Mendes, G. Prates, E. C. Pavlis, D. E. Pavlis and R. B. Langley, Geophys. Res. Lett. 29(10) (2002) 1414, doi: 10.1029/2001GL014394. 19. G. C. Hulley and E. C. Pavlis, J. Geophys. Res. 112 (2007) B06417, doi: 10.1029/2006JB004834. 20. T. Otsubo and G. M. Appleby, J. Geophys. Res. 108 (2003) B4, 2201.

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21. G. Delle Monache, The INFN-LNF Space Climatic Facility for LARES and ETRUSCO, Proc. 15th Int. Laser Workshop, Canberra Australia, Oct. 2006, CD-ROM (2007). 22. G. Kirchner, W. Hausleitner and E. Cristea, IEEE Trans. Geoscience and Remote Sensing 45(1) (2007) 201–205. 23. V. Burmistrov, N. Parkhomenko, Y. Roy, V. Shargorodsky and V. Vasiliev, NASA/CP-2003-212248 (2003). 24. J. Degnan, Geodynamics, Special issue on laser altimetry 34 (2002) 551–594. 25. A. Bosco et al., Int. J. Mod. Phys. D 16 (2007) 2271–2285.

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Advances in Geosciences Vol. 13: Solid Earth (2007) Ed. Kenji Satake c World Scientific Publishing Company


EARTH ROTATION PARAMETERS FROM VERY LONG BASELINE INTERFEROMETRY AND RINGLASER OBSERVABLES P. J. MENDES CERVEIRA∗ , H. SPICAKOVA and H. SCHUH Institute of Geodesy and Geophysics Advanced Geodesy, Vienna University of Technology Gusshausstrasse 27-29, Vienna, 1040, Austria ∗ [email protected] T. KLUEGEL Bundesamt f¨ ur Kartographie und Geod¨ asie BKG, Fundamentalstation Wettzell, Sackenrieder Str. 25 93444 Koetzting, Germany U. SCHREIBER and A. VELIKOSELTSEV Forschungseinrichtung Satellitengeod¨ asie FESG Fundamentalstation Wettzell, Sackenrieder Str. 25 93444 Koetzting, Germany

The primary goal of this chapter is to provide the mathematical formalism for the combination of Very Long Baseline Interferometry (VLBI) and ringlaser observations. First, we derived the partials of a simplified VLBI observation equation, w.r.t. nutation, sidereal time, universal time, length of day, and geodetic polar motion parameters. All the same, the partials for the ringlaser technique, compatible to VLBI parameterization, are presented. A combination of both techniques seems primordial, as the ringlaser technology may in the near future contribute effectively to the determination of subdiurnal Earth rotation variations, given its uniqueness in sensitivity to the instantaneous Earth rotation vector and its near real-time data acquisition. We describe the theoretical compatibility of both types of observables.

1. Introduction Very Long Baseline Interferometry (VLBI) can accurately measure the geodetic parameters associated with the shape of the Earth and its orientation in inertial space. This includes universal time variation (UT1– UTC), polar motion, and nutation/precession, i.e. the full set of Earth orientation parameters (EOP).5,8,13,17,20 In addition, VLBI is the only 155

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technique that provides access to the quasi-inertial reference system through observations of the extragalactic radio sources that form the Celestial Reference Frame (CRF). Throughout this chapter, we mean by geodetic polar motion the motion of the Celestial Intermediate Pole (CIP) w.r.t. a conventional Terrestrial Reference Frame (TRF),21 whereas the geophysical polar motion represents the motion of the instantaneous Earth rotation axis w.r.t. the same conventional TRF. Today, the geodetic polar motion and universal time observations with a temporal resolution as short as semi-hourly are obtained from VLBI, with the potential to reveal short period and episodic events with signatures below 100 µas (7.5 µs) level. Eight-hours variations in geodetic polar motion in the order of 40 µas15 have been reported during the VLBI campaign CONT02, with eight stations continuously observed from 16 to 31 October 2002. Today, data from a rather new technology, called ringlaser gyroscope is available.7 Ringlasers such as at Wettzell fundamental station (Germany) sense uniquely the signatures of the instantaneous Earth rotation vector in an Earth-fixed frame.2,5,7,14 The current situation is the following: up to now, the nontidal variations in Earth rotation at subdiurnal periods have remained undetected. In the subdiurnal band, one difficulty arises in the observation equation of VLBI because of the ambiguity between the geodetic polar motion and the nutation/precession parameters.1,14,17,18 In order to resolve this ambiguity, the scientific community adopted in the IERS Conventions 2003 a choice to separate both types of parameters depending solely on their frequencies, i.e. conventionally.9 As long as no external observations are able to distinguish between the physical meaning of both phenomena, this adoption is completely justified. In this respect the ringlaser technology could, in the near future, take over an important position in the determination of subdiurnal Earth rotation variations. For this reason, we will describe the theoretical compatibility of both types of observables (VLBI and ringlaser) enabling a combined determination of subdiurnal polar motion and universal time variations. Special attention has been dedicated to the application of identical reduction models.

2. Kinematics of Earth Orientation The VLBI technique is sensitive to the complete rotation matrix from a conventional TRF21 to a conventional CRF. Here, rotation means the temporal derivative of the orientation. A simplified “classical” parameterization based on the equinox will be used throughout this chapter.

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Although an alternative formulation based on the Non Rotating Origin (NRO) has recently been recommended by the International Astronomical Union (IAU), the scientific community usually still decides to apply the “classical” equinox-based theory. The latter is from the mathematical point of view equivalent to the NRO theory. The barycenter of the solar system and the center of mass of the Earth are the most appropriate origins for any celestial and terrestrial reference system, respectively. The ecliptic and the equator, affected by the complexities of the structure of the Earth and solar system, are fundamental planes directly related to the dynamics of the solar system.

2.1. Theoretical considerations Let Q be the transformation matrix from some conventional TRF21 to some conventional CRF.9 Then, any vector R in the TRF can be transformed to CRF, leading to the vector r, so that r = Q · R = P · N · U · X · Y · R,

(1)

where P, N, U, X, and Y account for precession, nutation in longitude ∆ψ and obliquity ∆ε, sidereal time (∆h), and geodetic polar motion (xp , yp ), respectively. Now, we assume that the orientation matrix Q0 is based on some set of excellent a priori Earth orientation models,3,4,9,12,16 so that only the uniform rotation, about a constant axis, described by U0 , is not an identity matrix. Then, to first order, we obtain18 : ∆Q = Q0 − Q = (∆P + ∆N ) · U0 + ∆h · ∆U0 + U0 · (∆X + ∆Y ),

(2)

with h = h0 + ∆h,

(3)

being the hour angle of the true equinox of date, and   cos h0 − sin h0 0 U0 =  sin h0 cos h0 0  , 0 0 1   − sin h0 − cos h0 0 ∆U0 =  cos h0 − sin h0 0  , 0

0

0

(4)

(5)

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

 1 ∆ψ · cos ε ∆ψ · sin ε , ∆N =  −∆ψ · cos ε 1 ∆ε −∆ψ · sin ε −∆ε 1   1 0 −xp ∆X + ∆Y =  0 1 yp  , xp −yp 1

(6)

(7)

where ε is the mean obliquity of the ecliptic. In the following, we assume that a deficiency in the variational precession matrix ∆P will be absorbed by the variational nutation matrix ∆N .

2.2. VLBI Since nearly three decades, VLBI has been successfully applied for measuring variations in all five components of Earth’s orientation. The basic VLBI observable is the delay τ in the time of arrival of a radio wavefront between two VLBI antennas. The geometric distance L, corresponding to the delay τ multiplied by the speed of light in vacuum c, can be related to the following approximate observation equation: L = c · τ = kT · Q · B,

(8)

where k(αδ) represents the Cartesian vector of the source position with right ascension α and declination δ in the CRF, and B(Bx , By , Bz ) the baseline vector in the TRF:   cos δ · cos α k =  cos δ · sin α  . (9) sin δ Assuming that all rotational motions affecting k and B have been properly reduced, the linearized version of the reduced observation equation reads to first order: ∆L = kT · ∆Q · B.

(10)

The partial derivatives of this reduced equation w.r.t. the five Earth orientation parameters can be derived by using Eq. (2): ∂(∆L) = sin δ · sin ε · [−Bx · cos h0 + By · sin h0 ] ∂(∆ψ) + cos δ · sin ε · cos α · Bz ,

(11)

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∂(∆L) = − sin δ · [Bx · sin h0 + By · cos h0 ] + cos δ · sin α · Bz , ∂(∆ε)

(12)

∂(∆L) = Bx · sin δ − Bz · cos δ · cos hs , ∂(xp )

(13)

∂(∆L) = −By · sin δ − Bz · cos δ · sin hs , ∂(yp )

(14)

∂(∆L) ∂(∆L) 1 = − cos δ · [Bx · sin hs + By · cos hs ] = · , ∂(∆h) f ∂(dUT1)

(15)

∂(∆L) = −f · cos δ · [Bx · sin hs + By · cos hs ], ∂(dUT1)

(16)

where f is the conversion factor from solar to sidereal time, i.e. 1.0027379093. The partial derivative of the equation of equinoxes w.r.t. the nutation parameter ∆ψ has also been taken into account in Eq. (11). The consequence is a zero mathematical correlation between the parameters ∆ψ and ∆h. The latter parameter is a function of the variation of universal time dUT1. The Greenwich apparent sidereal time hs has been used, i.e. hs = h0 − α. In view of a possible combination with ringlaser data, the required partials w.r.t. the time derivatives of polar motion and universal time are also set up: ∂(∆L) ∂(xp ) ∂(∆L) ∂(∆L) = · = · ∆t, ∂(x˙ p ) ∂(xp ) ∂(x˙ p ) ∂(xp )

(17)

∂(∆L) ∂(∆L) ∂(yp ) ∂(∆L) = · = · ∆t, ∂(y˙ p ) ∂(yp ) ∂(y˙ p ) ∂(yp )

(18)

∂(∆L) ∂(dUT1)

∂t

∂(∆L) = · ∂(dUT1)

∂(dUT1) ∂(dUT1)

∂t

=

∂(∆L) · ∆t, ∂(dUT1)

(19)

as well as partial w.r.t. length-of-day variations ∂(∆L) ∂(∆L) ∆t =− · . ∂(∆lod) ∂(dUT1) T0

(20)

In the last four equations we introduced the time interval ∆t. The nominal length of day is T0 , i.e. 86400 s SI.

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From the theoretical point of view, there is no problem to estimate corrections w.r.t. EOP with an amplitude of 20 mas to an accuracy of 1 µas without resorting to iterations, assuming no noise in the VLBI data. This restriction of 20 mas arises due to the linearization of the presented observation model, i.e. Eq. (2), and of the subsequent approximations for the nutation and polar motion incremental angles. However, this restriction is not applicable to current VLBI software packages, e.g. OCCAM,10 as numerical differentiations are applied to the nonlinear observation equation.

3. Ringlaser The Wettzell ringlaser “G”, located at the geographical position 49.145◦ North and 12.876◦ East, is an inertial rotation instrument, which uses the Sagnac effect that arises when two counter-rotating laser beams lead to a beat frequency upon a rotation variation.2,7 The Sagnac frequency ∆Sf relates the enclosed area A of the instrument, its perimeter (optical pathlength) Pr , the optical wavelength λopt , the unit normal vector n to the ringlaser area, formed by the beam path, and the rotation vector Ω w.r.t. a prescribed TRF21 : ∆Sf =

4·A · nT · Ω Pr · λopt

(21)

with 

cos ϕ · cos λ



  n =  cos ϕ · sin λ  sin ϕ



− cos ϕ0 · sin λ0 · ∆λtop − sin ϕ0 · cos λ0 · ∆ϕtop



  = n0 + ∆n = n0 +  cos ϕ0 · cos λ0 · ∆λtop − sin ϕ0 · sin λ0 · ∆ϕtop , cos ϕ0 · ∆ϕtop (22) where n0 is the reference unit normal vector with ϕ0 and λ0 being the nominal geographic latitude and longitude of the ringlaser position, respectively, and ∆ϕtop and ∆λtop their respective topocentric deflections of the local vertical. The instantaneous angular velocity vector of the Earth Ω can be parameterized by using a perturbation ∆Ω w.r.t. the reference

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angular velocity vector Ω0 : 

 0  Ω = R1 (my ) · R2 (mx ) ·  0 Ω0 + ∆Ω = Ω0 + ∆Ω   −Ω0 · mx ≈ Ω0 +  Ω 0 · m y  , ∆Ω

(23)

with  Ω0 = 0 0

Ω0

T

=

0 0

2π T0

T ,

(24)

where R1 and R2 represent the rotation matrices (counterclockwise being a positive rotation) about the x- and y-axis of the TRF, taking the geophysical polar motion angles my and mx into account. The Sagnac frequency ∆Sf itself is rarely used. The relative change of the Sagnac frequency ∆Sr is preferable, and is related to the relevant parameters my , mx , and ∆Ω by the following equation: ∆Sr =

∆Sf − ∆Sf 0 nT · Ω − nT 0 · Ω0 = ∆Sf 0 nT · Ω 0 0

=

(n0 + ∆n)T · (Ω0 + ∆Ω) − nT 0 · Ω0 nT · Ω 0 0

≈

∆nT · Ω0 + nT 0 · ∆Ω nT · Ω 0 0

≈ cot ϕ0 · [−mx · cos λ0 + my · sin λ0 + ∆ϕtop ] +

∆Ω , Ω0

(25)

where the last term can be linked to a variation of length-of-day ∆lod or universal time dUT1 through −∆lod ∂(dUT1) ∆Ω . = = Ω0 T0 ∂t

(26)

Equation (25) shows that, to first order approximation, the ringlaser is not sensitive to longitudinal tilt variations ∆λtop . For an elastic Earth, the local topocentric latitudinal deflection ∆ϕtop , if produced by the solid Earth tides, is related to the first order geocentric

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latitudinal variation ∆ϕ by ∆ϕtop ≈ −∆ϕ ·

1 + k2 − h2 l2

≈ −8.1163 · ∆ϕ,

(27)

where k2 = 0.29525, h2 = 0.6078, and l2 = 0.0847 are the nominal degree-2 Love and Shida numbers. Equation (27) allows to calculate topocentric latitudinal tilt variations due to the solid Earth tides from displacements on Earth’s surface as obtained from the most recent models,11 accounting for latitudinal and frequency-dependent Earth’s response. However, if the latitudinal variation ∆ϕ is caused by a surface load, such as an ocean tide load or atmospheric pressure load, then the latitudinal deflection of the local vertical ∆ϕtop is ∆ϕtop ≈ −

∞ 1  ∂Pn (cos ϑ) (1 + kn − hn ) ME n=1 ∂ϑ

∞

≈ −∆ϕ ·

(cos ϑ) + kn − hn ) ∂Pn∂ϑ ∞  ∂Pn (cos ϑ) n=1 ln ∂ϑ

n=1 (1

≈ −∆ϕ · G(kn , hn , ln , ϑ),

(28)

where the degree-dependent load Love numbers kn , hn , and ln are used as well as the partials of the Legendre polynomials Pn (cos ϑ) w.r.t. the spherical distance ϑ from the point mass load, and ME is Earth’s mass.22 The load Love numbers can be computed for specific Earth models in specific center of frames, e.g. the center of frame of the solid Earth or the center of mass frame of the Earth’s system including the load. Usually, the degree n goes up to 10,000, leading to a so-called Green’s function G(kn , hn , ln , ϑ) in Eq. (28), which can be computed once for all spherical distances ϑ from the point mass load, and used for the computation of ∆ϕtop if the latitudinal variation ∆ϕ is available from latitudinal surface deformations induced by surface loads. Finally, the reduced relative Sagnac frequency ∆SRLG (corrected for tilt variations ∆n of the unit normal vector n in latitudinal direction) is ∆SRLG = ∆Sr − cot ϕ0 · ∆ϕtop = cot ϕ0 · [−mx · cos λ0 + my · sin λ0 ] −

∆lod , T0

(29)

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and further ∆SRLG





 1 1 ≈ − cot ϕ0 · xp − y˙ p · cos λ0 + yp + x˙ p · sin λ0 Ω0 Ω0 −

∆lod . T0

(30)

In Eq. (30), a substitution of the geophysical polar motion parameters was applied: to first order approximation, the geodetic polar motion parameters obtained from space geodesy, xp and yp w.r.t. the CIP, are related to the geophysical polar motion parameters mx and my through the following formula1,14,17 :   1   y˙ x −  p Ω0 p  mx . ≈ (31)   1 my −yp − x˙ p Ω0 Equation (29) obviously shows that each ringlaser provides one observation for each epoch. However, there are three unknowns (mx , my , ∆lod) assuming that local position variations can be accurately monitored by external instruments, e.g. tiltmeters, or modeled theoretically. Thus, for one epoch, a minimum of three ringlasers at different geographic positions observing simultaneously could unambiguously determine these three unknowns. Equivalently, one ringlaser having three independently oriented beam circuits (i.e. three ringlasers oriented orthogonally at one site) would solve for the three unknowns. An interesting case is available when a ringlaser is mounted horizontally at the North Pole: it would be primarily sensitive to the length-of-day variations, as cot(90◦ ) = 0. From the practical point of view, Eq. (30) allows to calculate the relative Sagnac frequency variation from geodetic polar motion and ∆lod data, while Eq. (29) is applicable for theoretical models, such as effects accounting for solid Earth tides or Oppolzer terms.19,23 Both equations are needed for the reduction of ringlaser observations due to known effects, such as solid Earth tides, effect of ocean tides, and geodetic polar motion. Finally, we obtain the partial derivatives for the ringlaser w.r.t. geodetic polar motion (plus their respective rates) and ∆lod: ∂(∆SRLG ) = − cot ϕ0 · cos λ0 ∂(xp )

(32)

∂(∆SRLG ) = − cot ϕ0 · sin λ0 ∂(yp )

(33)

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1 ∂(∆SRLG ) ∂(∆SRLG ) = · ∂(x˙ p ) Ω0 ∂(yp )

(34)

1 ∂(∆SRLG ) ∂(∆SRLG ) =− · ∂(y˙ p ) Ω0 ∂(xp )

(35)

∂(∆SRLG ) 1 =− . (∆lod) T0

(36)

The main advantage of the ringlaser compared to space geodetic techniques is the high temporal data acquisition in quasi-near real-time and its direct relation to the instantaneous Earth rotation vector Ω. Equations (26) and (31) can also be used as condition equations for the combination of VLBI and ringlaser observations. Nutation parameters always have to be fixed, if subdiurnal geodetic polar motion is estimated. 4. Discussion and Conclusions We set up the mathematical formalism to connect VLBI and ringlaser observations, considering the difference between geodetic and geophysical polar motion. On the one hand, the detection limit of subdiurnal polar motion effects from VLBI reaches presently an accuracy of better than 3 mm on the Earth’s surface. With the upcoming VLBI2010 system,6 enabling an increased station network and providing antennas with faster slewing rates we are optimistic to significantly go beyond this detection limit. On the other hand, the subdiurnal relative Sagnac frequency variation obtained from the Wettzell G ringlaser, projected to Earth’s surface, today attains an accuracy of better than 3 cm. The gain of one order of magnitude within only a few years, makes this emerging ringlaser technology a serious complementary system to VLBI w.r.t. the determination of subdiurnal ERP variations. The combination of both systems for common and unique parameters of the Earth is one of the goals of the Global Geodetic Observing System (GGOS) initiated by the International Association of Geodesy (IAG). This chapter puts up succinctly a foundation for such a successful combination. Acknowledgments The authors are especially grateful to Richard Gross for the review of the manuscript. The first author is particularly indebted to the German Science Foundation (DFG, Deutsche ForschungsGemeinschaft) for funding

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this work within the Research Unit FOR584 “Earth Rotation and Global Dynamic Processes.”

References 1. A. Brzezinski and N. Capitaine, J. Geophys. Res. 98 (1993) 6667. 2. B. F. Chao, Trans. Am. Geophys. Union 72 (1991) 550. 3. B. F. Chao, R. D. Ray, J. M. Gipson, G. D. Egbert and C. Ma, J. Geophys. Res. 101 (1996) 20151. 4. C. Bizouard, R. Eanes, R. Ray, P. Brosche, P. Defraigne, S. Dickman, D. Gambis and R. Gross, IERS Conventions 2003 92 (2004). 5. H. Schuh, R. Dill, H. Greiner-Mai, H. Kutterer, J. Mueller, A. Nothnagel, B. Richter, M. Rothacher, U. Schreiber and M. Soffel, Mitteilungen des Bundesamtes f¨ ur Kartographie und Geod¨ asie 32 (2003). 6. J. Wresnik, J. Boehm, A. Pany and H. Schuh, Submitted to Ad. Geosci. 7. K. U. Schreiber, A. Velikoseltsev, M. Rothacher, T. Kl¨ ugel, G. E. Stedman and D. L. Wiltshire, J. Geophys. Res. 109 (2004). 8. M. Rothacher, G. Beutler, T. A. Herring and R. Weber, J. Geophys. Res. 104 (1999) 4835. 9. N. Capitaine, P. M. Mathews, P. Wallace, P. Bretagnon, R. Gross, T. Herring, G. Kaplan, D. McCarthy, B. Richter and P. Simon, IERS Conventions 2003 33 (2004). 10. O. A. Titov, V. Tesmer and J. Boehm, AUSLIG Technical Reports 7 (2001). 11. P. M. Mathews, V. Dehant and J. M. Gipson, J. Geophys. Res. 102 (1997) 20469. 12. P. M. Mathews, T. A. Herring and B. A. Buffett, J. Geophys. Res. 107 (2002). 13. P. J. Mendes Cerveira, J. Boehm, E. Tanir, J. Wresnik, H. Schuh and V. Tesmer, Geowissenschaftliche Mitteilungen 79 (2007) 209. 14. P. J. Mendes Cerveira, R. Weber and H. Schuh, VGI 2 (2007) 113. 15. R. Haas and J. W¨ unsch, J. Geodynamics 41 (2006) 94. 16. R. D. Ray, D. J. Steinberg, B. F. Chao and D. E. Cartwright, Science 264 (1994) 830. 17. R. S. Gross, Treatise Geophys. 3 (2007). 18. T. M. Eubanks, Geodynamic Series 24 (1993). 19. T. R. von Oppolzer, W. Engelman (2nd ed.), Leipzig 1 (1882) 152. 20. W. Schl¨ uter, E. Himwich, A. Nothnagel, N. Vandenberg and A. Whitney, Adv. Space Res. 30 (2002) 145. 21. Z. Altamimi, X. Collilieux, J. Legrand, B. Garayt and C. Boucher, J. Geophys. Res. 112 (2002). 22. W. E. Farrell, Rev. Geophys. 10 (1972) 751. 23. A. Brzezinski, Manuscripta Geodaetica 11 (1986) 226.

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Advances in Geosciences Vol. 13: Solid Earth (2007) Ed. Kenji Satake c World Scientific Publishing Company


TOWARD A NEW VLBI SYSTEM FOR GEODESY AND ASTROMETRY ¨ ¨ JORG WRESNIK∗ , JOHANNES BOHM, ANDREA PANY and HARALD SCHUH Institute of Geodesy and Geophysics, Vienna University of Technology Gusshausstr. 27-29, Vienna, 1040, Austria ∗ [email protected]

Simulations are carried out at the Institute of Geodesy and Geophysics (IGG), Vienna, to support the design of a new geodetic Very Long Baseline Interferometry (VLBI) system, VLBI2010. The main part of these simulations is a Monte Carlo simulator which produces artificial group delays by modeling the stochastic processes caused by the station clocks, the wet zenith delays, and additional system errors. The clocks are simulated with a random walk plus integrated random walk; the wet zenith delays are derived from a turbulence model; and the system errors are represented by white noise. For the evaluation of the Monte Carlo simulator, comparisons with the continuous VLBI observations of CONT05 have been carried out. They show that the simulator is performing realistically including the turbulence model that produces values for the atmospheric path delays. For the analysis of the simulated data, the Monte Carlo simulator is implemented in a modified version of the software package OCCAM. Because of limitations due to the huge number of observations in the OCCAM Gauß-Markov algorithm, the Kalman Filter approach was applied. Different variance rates for the stochastic parameters in the Kalman Filter solutions are tested, and the best values are used for the antenna slew speed test. Different schedules with antennas of different slew speeds (from 1.5◦/s to 12◦/s in azimuth and 0.7◦/s to 3.1◦/s in elevation) are compared w.r.t. baseline length repeatabilities. The investigation shows that there is hardly any improvement with antennas faster than 4.5◦/s in azimuth and 2.1◦/s in elevation, in using the present scheduling strategies.

1. Introduction Within the context of the International Association of Geodesy’s (IAG) project, Global Geodetic Observing System (GGOS), it has become clear that the modern space geodetic techniques should try to provide station coordinates and/or baseline length time series with an accuracy of better than 1 mm and velocities of clearly better than 1 mm/year. Only then, 167

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subtle effects such as non-linear station motions or sea-level rise can be detected. There has been a lot of discussion in recent years on how Very Long Baseline Interferometry (VLBI) could exploit its present resources more efficiently and how future VLBI networks should look to achieve this general goal of sub-mm accuracy. In October 2003, the International VLBI Service for Geodesy and Astrometry (IVS) installed Working Group 3 (WG3) “VLBI 2010” to examine current and future requirements for geodetic VLBI systems. Based on the final report of WG33 and in particular on the requests defined by the WG3 subgroup on “Observing Strategies” and to encourage the implementation of the recommendations of WG3, the IVS established the VLBI2010 Committee (V2C) as a permanent body of IVS.4 A new set of criteria to specify the next generation geodetic VLBI system was established by the V2C, mainly based on • the recommendations for future IVS products given in the IVS Working Group 2 Report (WG2)5 and the requirements of the Global Geodetic Observing System project (GGOS) of the International Association of Geodesy (IAG); • the science-driven geodetic goals outlined in the NASA Solid Earth Science Working Group Report (SESWG). The IVS V2C investigated the various facets that the new system needs to fulfill the requirements. The major focus is on reducing the main error sources stemming from the atmosphere, the instrumentation, and the structure of the radio sources. Furthermore, the following strategies are investigated: • a reduction of the random component of the delay measurement error, e.g. the stochastic properties of the clocks and the unmodeled variation in the atmosphere; • a reduction of systematic errors, e.g. the thermal and gravitational deflection of the antenna, drifts of the electronics, and radio source structure; • an increase of the number of antennas for geodetic VLBI and an improved geographic distribution; • an increase of observation density, i.e. number of observations per unit time; • a reduction of susceptibility to external radio-frequency interference. At the Institute of Geodesy and Geophysics (IGG), Vienna University of Technology, different simulations are performed to evaluate new observing

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strategies and schedules, to improve the modeling of troposphere refraction and clocks, to find the best antenna design and to optimize the network geometry. A sequence of software programs is used for the simulations. After scheduling the observations with SKED,6 they are transformed to the socalled NGS format and then used as input to the VLBI analysis software package OCCAM,2 which was adapted for our simulations. The main part of the simulation studies is the so-called Monte Carlo simulator which creates the artificial observations based on realistic properties of the wet zenith delays and clocks. The observed group delay minus computed group delay (o − c) can be described as follows: o − c = (wzd 2 · mfw 2 (e) + cl2 ) − (wzd 1 · mfw 1 (e) + cl1 ) + wnBsl .

(1)

In Eq. (1), wzd1,2 are simulated wet zenith delays based on the turbulence model;1 cl1,2 are simulated clock values modeled as random walk plus integrated random walk7 at stations 1 and 2 of each observation; and mfw1,2 (e) are the wet mapping functions for the elevation angle e, which are assumed to be errorless in our studies. For each baseline observation, an additional white noise wnBsl is added to model the instrumental errors of stations 1 and 2. The Monte Carlo simulator, implemented in OCCAM, imports wet zenith delay values from the turbulence model, creates clock values for each station and epoch, and adds white noise for each observation. The following criteria are used to evaluate the potential of the VLBI system: baseline length repeatabilities, the root mean square (rms) of the 3D station position residuals, formal errors and standard deviations of the Earth Orientation Parameters (EOP), and the standard deviation between the simulated stochastic processes (troposphere delays, clocks) and their estimates. In this chapter, we are mainly focusing on the baseline length repeatabilities and the rms of the 3D station position to evaluate and compare different observing strategies and scenarios. A comparison between simulated and real data is carried out first to evaluate the Monte Carlo simulator itself and the reliability of the simulated group delays.

2. Evaluation of the Monte Carlo Simulation The Monte Carlo simulator creates group delays for each baseline observation as described in Eq. (1). An evaluation of the simulator is important to get an idea about the reliability of the whole simulation

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Fig. 1. Station map with 11 VLBI sites for the continuous VLBI campaign CONT05 in September 2005.

work and the conclusions and/or decisions drawn from the investigations. Therefore, the Monte Carlo simulator is compared against real observations from CONT05, a two-week campaign of continuous VLBI observations from 12 September 2005 until 27 September 2005. The observed network consists of 11 stations (Fig. 1): Algonquin Park (Canada), Gilmore Creek (Alaska, USA), HartRAO (South Africa), Kokee Park (Hawaii, USA), Ny ˚ Alesund (Norway), Onsala (Sweden), Svetloe (Russia), TIGO Concepcion (Chile), Tsukuba (Japan), Westford (Massachusetts, USA), and Wettzell (Germany). The results of the CONT05 analysis are generally considered the best VLBI contribution to date. For the simulation, the schedule of the first 24 hours of CONT05 is taken, and wet zenith delays, clocks, and white noise are simulated for 15 days, to be as similar as possible to the real observations. The simulation of the variable wet zenith delay follows the theory of Treuhaft and Lanyi,8 which is based on the Kolmogorov turbulence theory.9 The refractive index structure function constant (Cn ) and the effective height of the wet troposphere, which are the most important input parameters for the turbulence model, are assumed to be constant over the 15 days for each station but wind speed and wind direction vary with time. Thus, the simulated delays will vary as a function of observed direction and as a function of time.1 This approach promises to provide very realistic wet path delays through a turbulent atmosphere. The clocks are simulated with a random walk plus integrated random walk corresponding to an Allan standard deviation (ASD) of 1 · 10−14 @50 min, which is a high-performing maser system representative

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25 CONT05 real data CONT05 simulated data

length repeatability [mm]

20 clocks: 1*10-14@50min wn: formal errors of NGSfile wzd: turbulence model 15

10

5

0 0

2

4

6 8 10 baseline length [1000 km]

12

14

Fig. 2. Baseline length repeatabilities of the real VLBI CONT05 sessions (filled circles) and the Monte Carlo simulation (filled squares). There is good agreement, especially for baselines shorter than 6,000 km.

for the present VLBI sites. The added white noise was taken from the formal errors of real observation files (NGS files) from CONT05. The results are presented in Fig. 2, where the circles show the baseline length repeatabilities of the CONT05 sessions and the squares correspond to the baseline length repeatabilities derived from the Monte Carlo simulation. The agreement for the shorter baselines is high. However, there is disagreement for a few baselines longer than 6,000 km, where the difference between the repeatabilities from real and simulated observations lies in a range of ±1 cm. For baselines shorter than 6,000 km, these differences are less than ±2 mm (Fig. 3). Generally, it can be concluded that the simulation of atmospheric path delays using the turbulence model creates realistic VLBI group delays for the Monte Carlo simulator. In parallel, similar studies are carried out at NASA/GSFC, Greenbelt, USA10 by using the VLBI analysis software SOLVE.

3. Comparison of Gauß–Markov LSM and KF in OCCAM The VLBI analysis software OCCAM is used by various IVS Analysis Centers for routine analysis. For the tasks described above, the official version of the software package was modified to implement the Monte Carlo

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CONT05 real data - CONT05 simulated data

diff. of length repeatability [mm]

6 4 2 0 −2 −4 −6 −8 −10 −12

0

2

4

6 8 10 baseline length [1000 km]

12

14

Fig. 3. Differences of the baseline length repeatabilities from the analysis of the real CONT05 data and the Monte Carlo simulation. For the simulation, the wet zenith delays are taken from the turbulence model, clock errors are simulated with an ASD of 1 · 10−14 @50 min, and for the white noise, the formal errors are taken out of the observation files (NGS files).

simulator. Due to very dense schedules with large numbers of observations, it was not possible to solve the normal equation system of the Gauß–Markov model as implemented in OCCAM. Thus, for the proposed simulation, we applied the Kalman Filter approach, which is also implemented in OCCAM.2 To compare the two different approaches, a test data set of 120 baselines was analyzed. The data set results from an observing schedule for a 16-station network with 9,678 observations (11 observations/hour/ station, 7 min and 20 s maximum time lag between two observations). The baseline length repeatabilities for the 120 baselines (Fig. 4), analyzed both with the Gauß–Markov and the Kalman Filter, show differences (Fig. 5) which are in the range of ±4 mm and to 67% positive in the sense Gauß–Markov minus Kalman Filter with a maximum reduction of variance of 40% in some cases. The reason for the Kalman Filter being superior to the Gauß–Markov method in most of the cases might be that the Kalman Filter estimates the wet zenith delays and clocks at every observation epoch, while in the Gauß–Markov method, wet zenith delays and clocks are estimated as continuous piecewise linear functions with time intervals of 30 min. This is a particular advantage of the Kalman Filter for

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12 Gauß Markov Kalman Filter

length repeatability [mm]

10

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4 Gauß Markov - Kalman Filter

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the simulation, because the resolution of wet zenith delays and clocks only depends on the observation density of the analyzed schedule and does not have to be adjusted for each schedule as in the case of the Gauß–Markov model.

4. OCCAM Kalman Filter Analysis with Different Variance Rates for Wet Zenith Delays and Gradients The troposphere wet zenith delay and gradient parameters are considered as random walk stochastic processes11 in the Kalman Filter method. The gradients describe the azimuthal asymmetry in the refractive index of the atmosphere. In the following, the variance rates for estimating wet zenith delay and gradients are varied to assess their influence on the solution. For this analysis, a schedule for a virtual 16-station network (Fig. 6) was produced. The virtual stations are chosen in a way to get good global station distribution. The only criterion for the additional sites which is required is an existing IGS (International GPS Service) station at this position. For the schedule, antennas with slew speeds of 12◦/s in azimuth and 3.1◦/s in elevation are used. The average number of observations/hour/station is 131. Figure 7 shows the baseline length repeatabilities, once without using gradients and twice with gradients estimated, where two different values for the variance rates of wet zenith delay and gradients were used: 0.1 ps2/s for wet zenith delays and 0.015 ps2/s for gradients (tight constraints), and

Fig. 6. Virtual 16-station test network for VLBI2010 simulations with very good global distribution.

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0.7 ps2/s for wet zenith delays and 0.5 ps2/s for gradients (loose constraints). The repeatabilities for the solution without gradients are in a range of about 7 mm for baselines longer than 12,000 km, and the improvement when using gradients can be seen very clearly and also the enhancement for loose constraints compared to tight constraints. The repeatability for the longest baseline improves by about 2 mm for the loose constraints. The baseline length repeatability is a good criterion to show the performance of the whole VLBI system, but it does not provide a single number to compare different solutions easily. Thus, the median of the root mean square (rms) values of the 3D station position residuals is calculated Table 1. Median rms of the 3D station position residuals analyzed with different values for the variance rates of wet zenith delays and gradients. Wet zenith delays [ps2/s]

Gradient [ps2/s]

Median rms [mm]

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(Table 1). Also, the rms values of the 3D station position residuals are plotted (Fig. 8) to reveal station-dependent behavior in the estimation.

5. Slew Speed Studies One of the main goals of the simulations is to obtain information about antenna specifications for the new VLBI2010 system by testing different slew speeds. The test values used range from 1.5◦/s to 12◦/s in azimuth and from 0.7◦/s to 3.1◦/s in elevation. The schedules for the analysis were produced by J. Gipson, NASA/GSFC, Greenbelt, USA. To get a dense schedule, about 100 of 230 radio sources were taken from a recently compiled and improved list of geodetic sources, and the on-source time was reduced to a maximum of 5–10 s. Statistics for the schedules are summarized in Table 2. As can be seen, the schedule performance improves steadily from the 1.5/0.7◦/s case to the 6.0/2.1◦/s case. But, improvement is negligible from the 6.0/2.1◦/s case to the 12.0/3.5◦/s case. This is because the on-source time and

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Table 2. Number of observations/hour/station (obs/h/st) and number of observations in total for different antenna slew speeds in azimuth and elevation. Slew speed az [◦/s]

el [◦/s]

1.5 3.0 4.5 6.0 12.0

0.7 0.7 2.1 2.1 3.5

Avg obs/h/st

Min obs/h/st

Max obs/h/st

No. of obs.

47 61 101 120 131

39 46 76 89 96

55 76 121 144 155

59392 83149 134134 159088 173831

accelerate/decelerate phases begin to dominate over the maximum slew speed phase of the observing cycle. More investigations on optimizing scheduling strategies have to be carried out. For the Monte Carlo simulations, the wet zenith delays are simulated using the turbulence model; the clocks are simulated with an ASD of 2·10−15@15 min, and the white measurement noise is simulated using a 4 ps 1-sigma Gaussian random variable. The very small white noise corresponds

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to that predicted for the new VLBI2010 antenna systems. Regarding the baseline length repeatabilities (Fig. 9), a very clear improvement from slow antennas to antennas with a slew speed of 4.5◦/s in azimuth and 2.1◦/s in elevation can be seen. For a better illustration, the linear fit of the baseline length repeatabilities is plotted separately in Fig. 10 which clarifies that the biggest improvement is gained when using antennas with the highest slew speed of 12◦/s in azimuth and 3.5◦/s in elevation, but the enhancement compared to slower antennas with a slew speed of 4.5◦/s or 6◦/s in azimuth and 2.1◦/s in elevation is relatively small. 6. Conclusions and Perspectives For the VLBI2010 committee, simulation studies are of very high interest because decisions about the next generation VLBI system will be mainly based on these results. Therefore, the Monte Carlo simulator has to be able to reproduce real observations. Hence a comparison with the continuous CONT05 VLBI campaign has been carried out, which shows a good agreement between real and simulated observations. For baselines up to

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6,000 km, the differences between the baseline length repeatabilities are in the range of ±2 mm, while for longer baselines, the range is ±1 cm. Since wet zenith delays have the main influence on the simulation results, they are simulated as realistically as possible. Thus, applying the turbulence model is very important for the Monte Carlo simulator. In the VLBI analysis software OCCAM, which was modified to analyze simulated observations, the Gauß–Markov model as well as the Kalman Filter is realized. In the comparison of both models, the Kalman Filter shows its advantages for simulation studies. With it, the resolution of wet zenith delays and clocks only depend on the observation density of the analyzed schedule and do not have to be adjusted for each different schedule as is the case for the Gauß–Markov analysis. Further investigations on the variance rates for wet zenith delay and gradients have been carried out to find the most suitable values. As a result, the repeatabilities are the best (2 mm for the longest baselines) when using variance rates of 0.7 ps2/s and 0.5 ps2/s for wet zenith delay and gradients, respectively. These can be called “loose constraints.” For comparison, the baseline length repeatabilities for solutions without gradients are in the range of about 7 mm, a factor of 3.5 worse. Furthermore, the median of the rms of the 3D station position residuals was compared for the three different solutions with a median of 2.37 mm for the solution without gradients, 1.29 mm for the solution with tight constraints, and 1.05 mm for the solution with loose constraints. Consequently, these loose variance rates were used for the determination of ideal slew speeds for the VLBI2010 antennas. Baseline length repeatabilities show a big improvement for antennas with a slew speed up to 4.5◦/s in azimuth and 2.1◦/s in elevation, but for faster antennas the enhancement is hardly significant. To profit from very fast moving antennas, more investigations on scheduling (e.g. reducing the idle time of the antennas) have to be carried out. All these simulations consist of only the geometry and the stochastic situations, but neglect other important facts such as mechanical, structural, and geological stability of an antenna or costs.

References 1. T. Nilsson, R. Haas and G. Elgered, Proc. 18th EVGA Working Meeting (2007), p. 175. 2. O. Titov, V. Tesmer and J. B¨ ohm, Proc. 3rd IVS General Meeting (2004), p. 267.

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3. A. Niell, A. Whitney, B. Petrachenko, W. Schl¨ uter, N. Vandenberg, H. Hase, Y. Koyama, C. Ma, H. Schuh and G. Tuccari, Report of Working Group 3 to the IVS Directing Board (2005). 4. D. Behrend, J. B¨ ohm, P. Charlot, T. Clark, B. Corey, J. Gipson, R. Haas, Y. Koyama, D. MacMillan, Z. Malkin, A. Niell, T. Nilsson, B. Petrachenko, A. Rogers, G. Tuccari and J. Wresnik, Submitted to IUGG Proc. 2007. 5. H. Schuh, P. Charlot, H. Hase, E. Himwich, K. Kingham, C. Klatt, C. Ma, Z. Malkin, A. Niell, A. Nothnagel, W. Schl¨ uter, K. Takashima and N. Vandenberg, International VLBI Service for Geodesy and Astrometry 2001, Annual Report NASA/TP-2002-210001 (2002), p. 13. 6. N. Vandenberg, SKED: Interactiv/Automatic Scheduling Program, Program Reference Manual, Goddard Space Flight Center (1999). 7. J. B¨ ohm, J. Wresnik and A. Pany, IVS Memorandum 2006–013v03 (2007). 8. R. N. Treuhaft and G. E. Lanyi, Radio Science 22(2) (1987) 251. 9. V. Tatarskii, Jerusalem Israel Program for Scientific Translations (1971). 10. D. S. MacMillan, Proc. 18th EVGA Working Meeting (2007), p. 163. 11. T. A. Herring, J. L. Davis and I. I. Shapiro, J. Geophys. Res. 95(B8) (1990) 12561. 12. L. Petrov, Proc. 18th EVGA Working Meeting (2007), p. 141.

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Advances in Geosciences Vol. 13: Solid Earth (2007) Ed. Kenji Satake c World Scientific Publishing Company


PERIODIC STATION MOTION IN GOTHENBURG OBSERVED WITH GPS — POSSIBLY RELATED TO HYDROLOGICAL PHENOMENA? R. HAAS∗ , N. TANGDAMRONGSUB, H.-G. SCHERNECK and J. JOHANSSON Department of Radio and Space Science Chalmers University of Technology, SE-413 26 G¨ oteborg, Sweden ∗ [email protected]

We analyze several years of GPS data recorded with roof-top stations in Gothenburg (Sweden) and permanent stations of the Swedish national reference network in the larger Gothenburg region. The derived station positions of several of the roof-top stations in Gothenburg reveal periodic horizontal motions with amplitudes of several millimeters and periodic vertical motions with amplitudes of up to 1.5 mm. The observed motions have primarily an annual period. We compare the GPS-derived station motions with hydrological information (ground water and river level) and meteorological information (air temperature and pressure). The results indicate that the GPS-detected motions might be related to hydrological phenomena.

1. Introduction The city of Gothenburg is located at the Swedish west coast. This region is geologically dominated by bedrock, sediments, and thick layers of postglacial clay and is classified as a region of moderate- to high-risk for ¨ river runs through the city landslides. The southern branch of the G¨ ota Alv center and transports the run-off into the sea. The central and oldest parts of the city are located close to the river. Here the buildings are constructed on sediments and postglacial clay with high porosity. Many of the outer parts of the city on the other hand are constructed on bedrock with only a thin or no soil cover at all. There are several ground water observation stations and a river level observing station in the Gothenburg area. In the South, East, and North of Gothenburg, there are observing stations of the national network of permanent reference stations for GPS in Sweden, SWEPOS.1 181

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The SWEPOS stations are equipped with geodetic monuments that are founded in bedrock. GPS observational data are continuously recorded within the SWEPOS network since several years. During the last years, additionally a number of roof-top stations for real-time kinematic services have been established in Gothenburg and are operated by the municipality of Gothenburg. Figure 1 shows a map of the Swedish west coast with three SWEPOS stations (VANE, BORA, ONSA) in the surrounding of Gothenburg depicted as black triangles enclosed by circles, five roof-top GPS stations in the city (BISK, FROL, GOTA, KORT, TRAK) depicted as black inverted triangles enclosed by circles, three ground-water-level stations (GW52, GW53, GW54), and the river-level station in Gothenburg harbor (RL),

LAKE VÄNERN

VANE

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GÖTA ÄLV RIVER

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GW54

GOTA TRAK

BORA

GW52

57˚30' ONSA

12˚30'

12˚00'

11˚30'

THE SEA

Fig. 1. Map of the Swedish west coast showing the location of the GPS stations used for this work (triangles — SWEPOS stations, inverted triangles — roof-top stations), and the ground water and river level stations. (See text for further explanations.)

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Geological and ground water situation at the GPS stations. Geological situation

Possible ground water withdrawal (l/h)

Soil cover

Bedrock

BISK

None or thin quaternary deposit

Oldest granite partly aged

600–2000

BORA

None or thin quaternary deposit

Orthogneiss partly aged

600–2000

FROL

None or thin quaternary deposit

Oldest granite partly aged

GOTA/TRAK

Sand, gravel postglacial clay

Oldest granite partly aged

600–2000

KORT

None or thin quaternary deposit

Oldest granite partly aged

600–2000

ONSA

None or thin quaternary deposit

Gabbro, diorit amfibolit

VANE

None or thin cover quaternary deposit

Oldest granite partly aged

<600

<600 600–2000

the latter depicted as black dots enclosed by circles. The sites GOTA and TRAK are located on the roof of the same building and were operated in parallel for some time. Table 1 summarizes the geological situation of the GPS stations in terms of soil cover and bedrock, and the amount of ground water that can be withdrawn. This information is taken from the interactive map service of the Geological Survey of Sweden, SGU.2

2. Hydrological and Meteorological Observations Figure 2 shows time series and amplitude spectra of ground water observations at three sites in the larger Gothenburg area, together with observations of the river level (see Fig. 1 for the locations of these sites). The ground water observation sites are located several kilometers apart from the GPS stations. Unfortunately, we do not have access to any ground water observation sites close to the GPS stations in the city center of Gothenburg and directly at the SWEPOS stations. Furthermore, we do not have any detailed information on the screen depth of the three sites. The only information that we have is that the aquifers are shallow.

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Table 2. Amplitudes and phases of annual signals in ground water (GW), river level (RL), temperature (T ), and pressure (P ). Station GW52 GW53 GW54 RL T P -ONSA

Amplitude (Aannual )

Phase (Pannual )

26.4 ± 2.1 cm 32.0 ± 4.8 cm 20.0 ± 2.8 cm 8.5 ± 0.5 cm 8.9 ± 0.1 C 0.9 ± 0.3 hPa

234 ± 5 doy 228 ± 9 doy 251 ± 8 doy 298 ± 3 doy 210 ± 1 doy 145 ± 18 doy

(Aug. 22) (Aug. 16) (Sep. 08) (Oct. 25) (Jul. 29) (May 25)

The river-level station is located in Gothenburg harbor and is affected not only by changes in river level but also by tidal effects from the sea. We are aware that the missing information and the quality of the ground water and river level sites restrict our current study. The time series cover 1999 through 2006, where we also have GPS data for most of the sites in the network. The dotted vertical line in the amplitude spectra indicates the annual period. Clearly, annual periodicities are visible from all four data sets. The results of a least-squares fit of annual signals in the time series are given in Table 2 as amplitude in centimeters and phase as day of the year (doy). Additionally, Fig. 2 shows time series and amplitude spectra of temperature and pressure at ONSA that can be regarded as representative for the region. The ground water levels peak in August and September, while the river level peaks in October. Temperature peaks already in July, while the pressure record has a low seasonal signal that peaks in May.

3. GPS Data Analysis and Results The GPS data were analyzed with the Bernese GPS software Version 53 using the network analysis strategy. The orbit and earth rotation products provided by the International GNSS Service (IGS)4 were used for the analysis, and station positions and velocities were a priori modeled in the IGS2000 reference frame. Ocean tide loading for all sites was modeled with parameters derived from the automated ocean tide loading provider6 using the GOT00.2 ocean tide model.7 Cycle slips were detected and removed, and the Quasi-Ionosphere Free (QIF) strategy was used to resolve the ambiguities using the IGS ionospheric model products. Tropospheric parameters were estimated and ionospheric free daily normal equations were

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North component (mm)

East component (mm)

Up component (mm)

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0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2

1.3 1.3 1.3 1.3 1.3 0.8 0.8 0.9

created. In the last step of the processing, these daily normal equations were stacked and station positions and velocities were estimated. Finally, daily station residuals were determined by Helmert transformations with respect to the combined solution. The median weighted root-mean square values of these three-dimensional residulas are given in Table 3 for all the stations in the network. They are smaller than half a millimeter for the horizontal components and between 0.8 and 1.3 mm for the Up components. In a post-processing step we removed a common-mode following the approach described in Ref. 8. Figure 3 shows time series of the resulting station residuals. Some stations experience seasonal signals in particular for the horizontal components. The five roof-top stations show larger variations in station position than the three SWEPOS stations. Furthermore, the Up components do show larger scatter than the horizontal components. Figure 4 shows amplitude spectra for all sites and components. Annual periods are indicated with vertical dotted line. Most sites and components show clearly annual signatures, where the stations GOTA and TRAK show the largest amplitudes, in particular for the horizontal components. Table 4 lists the annual amplitudes and phases derived from a least-squares fit to the time series of station residuals. Figure 5 visualizes the horizontal station residuals only in a two-dimensional representation. In particular, the two stations GOTA and TRAK show large residuals with a preferred distribution in the North–East direction. The stations BISK and FROL show North and North–West orientation of their horizontal residuals, while the other stations show smaller horizontal residuals only, and not so clear preferred orientations. The results of a least-squares fit to the horizontal components in terms of amplitudes and azimuth directions are given in Table 4.

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Fig. 3. Station residuals after common-mode removal. Shown are North, East, and Up components in (mm) in gray, and the corresponding 30 days running mean values in black. The left scale is relevant for the station residuals. To improve visibility, the residuals of all stations are offset by multiples of 5 mm from the ONSA residuals. Additionally, time series of ground water level, river level, and temperature are shown. Mean values are subtracted and offsets are introduced to improve visibility. The units are dm for the water levels, and deka ◦ C for the temperature. For the river level (RL) and temperature, again the running 30 days average values are shown in black.

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Fig. 3.

(Continued)

4. Conclusion and Outlook The analysis of GPS data in the Gothenburg region reveals for several of the roof-top stations significant horizontal station motions with predominantly an annual period. These periodic horizontal motions reach the order of several millimeters and are largest for the roof-top stations that are mounted on buildings in the city center that are constructed on a clay layer (Table 5). The roof-top stations mounted on buildings that are constructed on bedrock show significantly smaller horizontal motions, and the three SWEPOS stations show the smallest amplitudes. The predominating direction of the horizontal motions are North–East for the stations GOTA and TRAK, North for the station BISK, and North– West for the station FROL. The two stations TRAK and GOTA, located on the same roof, show very similar, however not identical station motions. These two stations were operated at different periods, and the overlap of their operation periods is only slightly more than a year. Thus, completely identical results cannot be expected. The vertical station components show much more scatter than the horizontal ones. The detected annual signatures in the vertical components are much smaller than the horizontal signatures and hardly significant (regarding the wrms values of the original GPS data analysis, see Table 3).

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Fig. 4. Amplitude spectra of the North, East, and Up components for the stations GOTA, TRAK, KORT, BISK, FROL, ONSA, VANE, and BORA, bottom to top. To improve visibility, the residuals of all stations are o