Input.Output Inc - Noise Edit 131970 IMG R3 DP

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Noise Edit Algorithm This chapter provides a detailed description of the Noise Edit algorithm and information on how and when to use it. Use the Noise Edit algorithm with vibroseis correlation and vertical stacking to edit or suppress short-period noise prior to correlation. When used properly, the Noise Edit module provides the ability to significantly improve the seismic signal quality and resolution. When used improperly, the Noise Edit module has the potential to degrade the seismic signal.

1.1

When To Use The Noise Edit algorithm is effective only when the noise has a short period and high amplitude relative to the seismic signal and occurs randomly on the channel. When doing a vertical stack, it is essential that noise edit be used when the above noise conditions are present. Some feel that noise edit should be used with diversity stack. If using the noise edit with diversity stack when the noise is long period and comprises a major portion of the channel, the result of the correlation will have either dead portions or low amplitude segments on the channel. Since diversity stack assumes that the signal is lower in amplitude than the noise, these low amplitude or zero segments appear as signal to the diversity stack algorithm. Input/Output has found that diversity stack works best without using noise edit. This also holds true when doing diversity stack after correlation.

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1.2

Description of Algorithm

1.2.1

Noise Detection Historically, most noise edit algorithms depend upon the acquisition of a history of gate powers computed with values corresponding to a predefined set of time-offset gates that were updated for each new source point as it was acquired. Due to effects caused by geology changes and the possibility of various degrees of offset changes that may occur in a prospect, the I/O I/O IMAGE™ System Release 3 uses the following approach. A history of the gate power for each channel is computed from the first two records of a stack and is then applied to these records. The remaining records are processed in a like manner and the history is updated after each record. This approach works well because of the randomness of the noise. The operator supplies a threshold value that the software uses to derive a reference to compare to the power values calculated from the channel being edited. Any power values on the channel that exceed the corresponding reference value are considered as noise. Below is a description of the steps used to detect noisy channels.

1.2.2

Gates Each channel is divided into gates containing an equal number of data samples. For example, with a gate length of 50 milliseconds (ms), a 10 second record at 2 ms sample interval has 200 gates. Each gate in this example has 25 data samples.

1.2.3

Gate Power The gate power level is calculated by summing the squares of all the data samples in a gate and dividing by the number of points in the gate as follows:

where

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Px = power for gate x, S1 = sample 1 in gate x, and n = number of samples in a gate. The initial gate power history is calculated using the first two records of a vibrator point (VP). The gate powers are calculated for each channel. Each channel with the same channel number is compared to its mate and the smaller power is retained as the historical value. The following methods are provided by the NOISEDIT module: • BURST EDIT—when a burst of noise is detected by the noise detection

algorithm, the gate in which the noise is detected is zeroed. The point at which the zeroing starts is determined by looking for the first zero crossing preceding the noisy gate. The point at which the zeroing ends is determined by checking the following gates to see if they also contain noise. A search is then performed to find the zero crossing starting at the last noisy gate end. • DIVERSITY NOISE EDIT—if a noisy trace is detected by the noise detection algorithm, each gate is scaled by the gate inverse power that was calculated from the trace itself. If noise was detected for a particular gate, the power for the noisy gate is replaced by the historical gate value. The trace is then recovery scaled using the edited gate power values. This results in the noisy gates being suppressed relative to the signal. When a channel is found to have noise, the following calculations are used to scale and recovery scale the channel.

1.2.4

End-point Scalars The end-point scalars are the reciprocal of the average of the sum of the squares of the power of the two adjacent gates as follows:

where Epx = end-point scalar for gate x, Px = average power of gate x, Px+1 = average power of gate x+1, and

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c = inverse scaling constant. The first end-point scalar (Ep0) of the channel will be set to the value of the first gate Ep1.

The last end-point scalar for a channel is given by:

1.2.5

Intermediate Sample Interpolation Increments The increment is a value that is used to calculate intermediate sample values between the respective end-point values on either side of the sample. To calculate the increment for gate x, the scalar Epx is subtracted from the scalar Epx-1, and divided by the number of samples in the gate as follows:

where Ix = increment for gate x+1, Epx = end-point scalar for the end of gate x, Epx-1 = end-point scalar for the end of gate x-1, and n = number of data samples in a gate. Figure 1-1 shows the relationships between the gate power, end-point scalars and increment values.

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Figure 1-1. Relationship Between the Gate Power (Px), End-Point Scalars (Epx), and Increment Values (Ix)

1.2.6

Inverse Scale Channel The input channel is scaled by first multiplying the end-point scalar increment for the gate of the specified data sample by the gate sample position. The product is added to the previous gate end-point scalar as follows:

where Sry = scalar for sample y of gate x, y-1 = sample offset from the first sample in the gate, Ix = increment for gate x, and Epx-1 = end-point scalar for gate x-1. Next, the corresponding channel value is multiplied by the scalar and the result is an inverse scaled sample value as follows:

where Riy = inverse scaled sample for sample y for gate x, Sy = input sample for sample y of gate x, and

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Sry = scalar for sample y of gate x.

1.2.7

Recovery Scaling The input channel is then recovery scaled by the edited gate powers of the input channel. The gate powers are edited by comparing the input channel gate powers with the reference gate powers. If an input channel gate power exceeds the reference then that gate power is replaced with the historical gate power as follows:

where Rx = reference value for gate x, Hx = historical power value for gate x, and T = threshold value. The power for each gate to be used for recover scaling are determined by: IF (Px > Rx) THEN Rpx = Hx ELSE Rpx = Px where Rpx = edited power of gate x, Px = power of gate x, Rx = reference power of gate x, and Hx = historical power of gate x. The recovery scaling increment values are calculated using the recovery scalar gate powers as follows:

where Irx = increment value for gate x recovery scaling, 16

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Rpx = edited power of gate x, Epx-1 = end-point scalar for gate x-1,and n = number of samples in gate. Next, recovery scalars are calculated using the recovery scaling increment values as follows:

where Rsy = recovery scalar for sample y of gate x, y = sample offset from first sample in gate x, Ix = increment value for gate x, and Rpx-1 = edited gate power for gate x-1. Next, each sample is divided by its respective recovery scalar value as follows:

where Rey = recovery scaled channel for sample y, Riy = inverse scaled sample y, and Rsy = recovery scalar for sample y.

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