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2D SRME Multiple Prediction465 SeisSpace Tools Reference

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2D SRME Multiple Prediction

Surface-related multiple elimination (SRME)is a methodfor attenuating multiples generated by the free surface. Ituses only the recorded data to predict all orders of freesurface multiples. Due to timing and amplitude errors thatarise in practice, the predicted multiples are typicallysubtracted from the data using adaptive filtering in aseparate flow. 2D SRME Multiple Predictionis intended forzero azimuth 2D towed streamer acquisition.

Theory

The principal advantage of SRME over Radon, deconvolution,or model-based methods is that SRME makes noassumptions about moveout or periodicity, and it requires nosubsurface information. 2D SRME will predict all surface-related 2D multiples, provided all the necessary subeventsare recorded within the aperture and azimuth of theacquisition line. The surface is assumed to be a perfectlyreflecting boundary and the input data are assumed to havebeen regularized prior to the application of 2D SRME. Shotand Receiver Regularization for 2D SRME will ensure thedata are prepared correctly for 2D SRME Multiple Prediction.The implementation assumes zero azimuth acquisition (thatis, the cable should be towed directly behind the sourceboat). Sources and receivers must be sampled at equalincrements, and near offsets must be extrapolated back tothe source. The traces within the shot must be ordered fromnear to far offsets. Preprocessing usually involves swell noiseattenuation and direct wave muting.

The implemented algorithm is

where M is the predicted multiples and D1and D2are the

input datasets. If only one dataset is input, then D2=D

1. The

variables xs, xgare the source and receiver coordinates and
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is the angular temporal frequency. Nxis the number of

traces summed in the multiple contribution gather. Thisequation implies a line source. The input datasets shouldhave their direct wave removed, and for optimal results thesource wavelet should be deconvolved and the ghosts

removed. For a more exact prediction, the deghosted datasetscould be redatumed to the free surface and an obliquityfactor could be applied to one of the two input datasets priorto 2D SRME Multiple Prediction. It is common practice toskip the deconvolution and deghosting and to let adaptivesubtraction compensate for the resulting amplitude andphase errors.

For multiple contribution gathers, the algorithm is

where D1and D2are the input datasets (in the case of one

input, D2=D1), and xsis the chosen output shot record

location, xgrepresents the receiver locations, and x

represents the surface locations within the aperture.

Reference

Verschuur, D.J., Berkhout, A.J., and Wapenaar, C.P.A. (1992) "Adaptive surface-related multiple elimination," Geophysics 57, 9, 1166-1177.

Weglein, A.B., Gasparotto, F.A., Carvalho, P.M., and Stolt, R.H. (1997) "Aninverse-scattering series method for attenuating multiples in seismic reflectiondata," Geophysics 62, 6, 1975-1989.

UsageFor 2D SRME Multiple Prediction, an example flow mayconsist of the following.

1. JavaSeis Data Input

2. Load distributed array

3. Shot and Receiver Regularization for 2D SRME (forward)

4. 2D SRME Multiple Prediction 5. Shot and Receiver Regularization for 2D SRME (reverse)

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6. Unload distributed array

7. JavaSeis Data Output

The 2D SRME Multiple Prediction tool is designed for typicaltowed streamer marine acquisition.

Data Organization

The input data organization must be one of the following.

The predefined MARINE_SHOTS_2D data organization.This is a logical subset of typical 3D marine field geome-

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tries where the five axes are TIME_IND, CHAN,SHOT_2D, CABLE_NO and S_LINE.

A 3D dataset corresponding to typical 2D marine geome-tries where the first two axes are TIME_IND, CHAN andthe third axis is either FFID or SOURCE.

Headers Used

None

Headers Added

If multiple contribution gathers is selected, SEQ_NO will beadded if it does not exist.

Headers Modified

None

Parameters

Available parameters vary based on the choices made.

Input data option

Choose from the following options.

One inputconvolves the input data with itself.

Two inputsconvolve two input datasets together.

To prepare two inputs, useJavaSeis Data InputandJavaSeis Data Match, with the latter tool parameterizedto combine capsules. Alternatively, computing higherorder terms in the multiple prediction series can besimulated by outputting Input and predicted multiples

and cascading multiple 2D SRME Multiple Predictiontools together. For this case be sure to set the parameterIdentifier for second input in data capsuleto a valueof srmultiples.

Note: Use the SeisSpace Check Toolutility to displayhow the datasets are being created and modified throughthe flow, including the name of the capsule identifier.

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Output data option

Predicted multiplesreturns only the predicted multi-ples.

Input and predicted multiplesreturns the input data

followed by the predicted multiples. If two inputs werechosen, it returns the first input along with the predictedmultiples. Note that the input will be filtered if either theMinimum frequency or the Maximum frequency are notset to the default.

Multiple contributiongathers returns, for one shot, thetraces contributing to the multiple prediction prior tosummation. This option may be used to investigate aper-ture.

Identifier for second input in data capsule

Appears if Input data optionisTwo inputs. Uses the firstinput by default. User must enter the name of the secondinput dataset. Enter the identifier for the second input in thecapsule. Defaults to seismic_2.

Note: Use the SeisSpace Check Toolutility to display howthe datasets are being created and modified through the flow,

including the name of the capsule identifier. In most cases,the ID will be seismic_2. This input will be removed after it isused by the tool.

Minimum frequency

Minimum frequency in Hz. Defaults to 0.0. Must be less thanthe maximum frequency.

Maximum frequency

Maximum frequency in Hz. Defaults to 0.0, which isinterpreted as the Nyquist frequency.

Percent padding in time

Percent increase in the number of samples to pad before fastFourier transform. Defaults to 100.0.

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Check shooting direction

IfYes, the tool examines trace headers to verify that the boatis pulling the cable and that the traces are ordered from nearto far within a shot. If the header values indicate the boat isnot pulling the cable, then the tool will fail during the Run

phase while printing the relevant header information to thelog file.

Output shot record

Appears if Multiple contribution gathersfor Output dataoption. Enter the identification number of the shot record (1,17, 234....) For each channel in the shot, an ensemble oftraces will be output.

Advanced Parameters

Aperture specification

Sets the definition of the aperture. Choose from the followingoptions.

Maximumapplies the maximum aperture, which willinclude all recorded offsets and will use negative offsetsby invoking reciprocity.

Nominal includes all the recorded offsets between thesource and the receiver.

Offset variableapplies an aperture that varies with theoffset of the output trace.

Aperture around midpoint

Appears if Offset variableis selected. Enter the size of the

aperture as a percentage of the offset. A value of 100% isequivalent to the Nominal aperture specification option.

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Minimum aperture

Appears ifAperture specificationis set to Offset variable.Enter the minimum number of traces to include in themultiple prediction.

Taper of multiple contribution gather traces

Specifies a percentage of the number of traces to taper ateither end of the multiple contribution gather. A setting of

0.0% means no taper while 10.0% will taper 10% on eitherside and leave the center 80% untapered. This action alwaysprecedes any normalization.

Apply fold normalization

Appears if Output data optionis set to Predicted multiplesor Input and predicted multiplesandAperturespecificationis set to Offset variable. SelectYesto dividemultiple prediction trace by the number of traces in themultiple contribution gather. See the equation above, where

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Nx represents the number of traces summed in the multiple

contribution gather.

Apply amplitude normalization

Appears if Output data optionis set to Predicted multiplesor Input and predicted multiplesandAperturespecificationis set to Offset variable. SelectYesto scale themultiples to have the same frequenced-averaged maximumamplitude as the (first) input dataset. The dataset ismultiplied by a single scalar to approximately match theamplitude of the original data. the scalar value is written tothe Info level of the log file. See Logging options.

Reverse polarity of the predicted multiples

Appears if Output data optionis set to Predicted multiplesor Input and predicted multiples. SelectYesto multiply thepredicted multiples by -1.

Apply 3D to 2D correction

Appears if Output data optionis set to Predicted multiplesor Input and predicted multiples. SelectYestoapproximately correct from 3D to 2D propagation. An

approximate correction is performed by dividing the data by. The correction is subsequently removed from the

predicted multiples by multiplying by .

Logging Options

Log on master joblet only

ChooseYesto receive logging messages from only the master.

Choose Noto receive logging messages from all joblets.

Logging verbosity

Selects the level and volume of debugging information.Choose one of the following options.

INFOprovides minimal diagnostic information.

DETAILprovides detailed diagnostic information appro-priate to diagnosing the effectiveness of test runs. Use

i

i

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this level to help determine if the chosen parameters areconverging on an optimal solution.

DEBUGprovides diagnostic information for trouble-shooting the program.

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