srme.pdf

ASEG 15th Geophysical Conference and Exhibition, August 2001, Brisbane. Extended Abstracts

Surface-Related Multiple Elimination Applications to an offshoreAustralia data set

Andrew S. Long Roald van Borselen Leharne FountainPGS Seres AS, Australia PGS Seres AS, England PGS Seres AS, [email protected] [email protected] [email protected]

INTRODUCTION

Removal of free-surface multiples from seismic reflectiondata is an essential pre-processing step in seismic imaging inoffshore Western Australia. Due to the high velocitycontrasts at the water bottom, first layer multiples tend todecay slowly and degrade the quality of a large part of theseismogram severely. In addition, peg legs are generated offstructurally complex 3D sedimentary bodies to create acomplicated set of reverberations that can easily obscureprimary reflections from relatively weak sedimentaryreflectors.

In complex geological environments where primary/multipleenergy ratios are generally low, it is essential to employmultiple elimination methods that require no a prioriinformation, either structural or material, about thesubsurface geology, and which leave unaffected all relevantinformation present in the data.

Surface Related Multiple Elimination (SRME) removes allmultiples that are introduced by a particular surface in theEarth. In order to remove these multiples, both the geometryand the reflection coefficients at this surface need to beknown. Since this information is readily available for thewater surface, it is possible to remove all multiples that aregenerated by the water surface, without using any additionalinformation about the subsurface. The method is fully data

driven, meaning that only the data itself is used to predict themultiples. As a result, user interaction is minimized.

In the following, we discuss the basic methodology ofSRME, and show some results of its application to a datasetfrom the NW Shelf in offshore Western Australia.

SURFACE-RELATED MULTIPLEELIMINATION

Surface-related multiple elimination is applied in three steps(Verschuur and Berkhout, 1997). The first step includes theremoval of all non-physical noise, regularisation of themeasured data to obtain a constant grid of sources andreceivers, the interpolation of missing near offsets andmissing intermediate offsets, and the removal of the directwave and its surface reflection. Since the method is data-driven, the quality of the data after multiple removal dependsheavily upon the pre-processed data.

The second step is the prediction of multiples. Theprediction is based on the observation that any surface-related multiple can be predicted through temporal andspatial convolutions of the measured wavefield with itself(Berkhout, 1982).

In the last step, the predicted multiples are subtracted fromthe input data, using the minimum energy criterion, whichstates that, after the subtraction of the multiples, the totalenergy in the seismogram should be minimized.

For a long time, the SRME method has been considered to bepromising but too expensive and too difficult to run inproduction processing. However, due to both increasedcomputer performance and increased understanding of thecrucial data preparation steps, the industry seems to bemoving towards a broader application of the method, and ithas even replaced more conventional methods in someonboard processing projects.

Current acquisition configurations prohibit the application of3D SRME. By assuming that no lateral variation occurs inthe cross-line direction, each individual streamer from a 3Dsurvey is assumed to pertain to a 2.5D configuration. Afteran inline projection of each streamer, the 2D SRME methodcan be applied. Small deviations from the 2.5D assumptioncan be overcome in the adaptive subtraction process.However, it is important to realize that most conventionaldemultiple methods based on predictive deconvolution anddifferential move-out filtering intrinsically assume localized1D configurations, thereby ignoring any inline variation.

SUMMARY

The presence of free-surface-related wave phenomena is aclassic problem in marine seismic data processing. Overthe years, the industry has relied heavily on conventionalmultiple suppression methods such as predictivedeconvolution and differential move-out filtering toremove surface-related multiples from marine seismicdata. These methods are based on rather specificassumptions about the subsurface and characteristicdifferences between primaries and multiples. Since theseassumptions are often not met in the field, theeffectiveness of these methods may be limited. Surface-Related Multiple Elimination (SRME) is a relatively newmethod that removes all surface-related multiples,without using any additional information about thesubsurface. Application of SRME to offshore Australiadata sets results in much improved results, whererelatively weak primary reflections become moreinterpretable.

Key words: Multiples, autoconvolution, SRME.

SRME applications to Australian offshore data Long et al.


RESULTS

SRME was applied to a 2D seismic line from the CarnarvonBasin (NW Shelf area). Data from this area is known to beseverely contaminated with multiples that are generated bynear-surface carbonates. A very strong reflection coefficientat the top and bottom of the carbonates results in a strongsurface multiple problem, and strong interbed multiples arealso a severe problem. Removal of multiple energy remainsthe foremost obstacle to successful seismic imaging inoffshore Western Australia. The full mechanism forgenerating the multiple wavefield has never been properlydetermined, nor has a satisfactory means been developed toremove the multiples. The water bottom in the survey area isvery shallow (75 m), resulting in a strong train of shortperiod multiple energy (refer to Figure 1). Event amplitudesare characteristically strongest at the near- to mid-offsets.

The following pre-processing steps were applied: Muting ofthe direct arrival and its surface reflection, removal ofrefracted wavefields, wavefield regularization, anti-aliasingfiltering and near offset interpolation.

A single shot gather from the first line is shown in Figure 1.The raw shot is shown in (a), the multiples predicted bySRME in (b), the result after subtraction of the predictedmultiples in (c), and the difference before and aftersubtraction is shown in (d). The improvement in data qualityafter SRME has been applied is easily observed. Forexample, the reflector at 1.8 s TWT becomes more prominentafter SRME has been applied (annotated on Figure 1).

Autocorrelations of a single shot gather, both before and afterthe application of SRME, are illustrated in Figure 2. Noteagain the strong reduction of multiple energy.

Figures 3a and 3b show the stacked sections of the raw data,and after SRME respectively. Note the significant reductionof multiple energy in the target area at about 1.8 s TWT, andthe corresponding improvement in the strength andcontinuity of the primary reflectors. Figure 3c shows thesame stack processed through a Tau-P deconvolution andradon demultiple process for comparison purposes.Examination of the different results indicates that SRMEyields improved primary event strength and continuity for allarrival times, and the stack has an overall reduction in highfrequency noise. Artifacts associated with the subtractionprocess appear to be negligible.

Note that the Tau-P deconvolution and radon demultiplemethod was computationally more expensive, and parametertesting for deconvolution and radon demultiple was quitetime consuming. In contrast, little paramateriztion testing isrequired for SRME. Furthermore, the preservation ofamplitudes for all offsets is superior with SRME.

CONCLUSIONS

Computational advances and increased understanding of thecrucial preparation steps are responsible for an increasedinterest in the application of the SRME method to large 3Ddata volumes. SRME requires no a priori information aboutthe subsurface, and as such it is fully data-driven. As aresult, very limited user interaction is needed.

Application of the method to a dataset from the NW Shelf,offshore Australia with SRME leads to satisfactory results: Asignificant reduction of multiple energy is obtained andrelatively weak primary reflections become moreinterpretable.

ACKNOWLEDGEMENTS

The authors thank PGS Australia Pty. Ltd. for permission topublish these results.

REFERENCES

Berkhout, A. J., 1982, Seismic Migration, Imaging ofacoustic energy by wavefield extrapolation, vol. 14A:Theoretical aspects, Elsevier, Amsterdam.

Verschuur, D. J., and Berkhout, A. J., 1997, Estimation ofmultiple scattering by iterative inversion, Part II: Practicalaspects and examples: Geophysics 62, 1596-1611.



Figure 1. A single shot gather from a 2D dataset in the Carnarvon Basin, NW Shelf Australia. The raw shot is shown in (a).Multiples predicted from the raw gather by SRME are shown in (b). The shot gather after the predicted multiples have beensubtracted is shown in (c). Note the improved strength of primary events (arrow). The gather depicted in (d) is the differencebetween (a) and (c).

Figure 2. Autocorrelations of a single shot gather from 2D data shown in Figure 1. The autocorrelation of the raw shot isshown on the left. The autocorrelation of the same shot after SRME is shown on the right. The ringing at near offsets evidentin the raw shot has been eliminated by SRME.

(a) (b)

(c) (d)



Figure 3. Stacked sections of 2D data from the NW Shelf, offshore Western Australia. The raw stack is shown in (a) and thestack after SRME has been applied is shown in (b). For comparison purposes, the stack after Tau-P deconvolution and radondemultiple is shown in (c).

(a)

(b)

(c)

Documents

srme.pdf