逆数据域表面多次波压制方法

逆数据域表面多次波压制方法Abstract

Surfacemultipleattenuationisamajorobstacleinseismicdataprocessing.Conventionalmethodshavelimitationsinsuppressingmultiplesadequately,especiallyingeologicalareaswithcomplexstructures.Inrecentyears,theinversedatadomainmethodhasbeenproposedasaneffectivewaytoreducesurfacemultiples.Thismethodinvolvestransformingthedataintoanewdomain,wherethesurfacemultiplesignatureisminimized,andthentransformingitbacktoitsoriginaldomain.However,therearestillchallengesinimplementingthismethod,includingthedesignoftheinverseoperatorandtheselectionoftheoptimalregularizationparameter.Inthispaper,weproposeanewmethodforinversedatadomainsurfacemultipleattenuation.Ourapproachisbasedonasparsityconstraintandarankconstraintintheinverseoperatordesign.Wealsopresentanewmethodforselectingtheoptimalregularizationparameterbasedonthetruncatedsingularvaluedecompositionmethod.Theperformanceoftheproposedmethodistestedonsyntheticandrealdata,andtheresultsdemonstrateitseffectivenessinreducingsurfacemultiplesandpreservingprimaryreflections.

Introduction

Surfacemultipleattenuationisanimportantprobleminseismicdataprocessing.Surfacemultiplesaremainlycausedbythereflectionofseismicenergyfromtheair-seainterfaceorthefreesurfaceoftheearth,andtheyofteninterferewiththeinterpretationofprimaryreflections.Variousmethodshavebeenproposedforsurfacemultiplesuppression,includingpredictivedeconvolution,wavefieldseparation,andmodel-basedmethods.However,thesemethodshavelimitationsinsuppressingmultiplesadequately,especiallyingeologicalareaswithcomplexstructures.

Inrecentyears,theinversedatadomainmethodhasbeenproposedasaneffectivewaytoreducesurfacemultiples.Thismethodinvolvestransformingthedataintoanewdomain,wherethesurfacemultiplesignatureisminimized,andthentransformingitbacktoitsoriginaldomain.Theinversedatadomainmethodhasadvantagesoverothermethodsintermsofeffectivenessandcomputationalefficiency.However,therearestillchallengesinimplementingthismethod,includingthedesignoftheinverseoperatorandtheselectionoftheoptimalregularizationparameter.

Inthispaper,weproposeanewmethodforinversedatadomainsurfacemultipleattenuation.Theproposedmethodisbasedonasparsityconstraintandarankconstraintintheinverseoperatordesign.Wealsopresentanewmethodforselectingtheoptimalregularizationparameterbasedonthetruncatedsingularvaluedecompositionmethod.Theperformanceoftheproposedmethodistestedonsyntheticandrealdata,andtheresultsdemonstrateitseffectivenessinreducingsurfacemultiplesandpreservingprimaryreflections.

Methodology

Theinversedatadomainmethodinvolvestransformingthedatafromthetimedomaintoanewdomain,wherethesurfacemultiplesignatureisminimized,andthentransformingitbacktothetimedomain.Thetransformationisachievedbydesigninganinverseoperatorthatmapsthedatafromthetimedomaintothenewdomainandback.Theinverseoperatoristypicallydesignedtominimizetheenergyofthesurfacemultipleinthenewdomain.

Theproposedmethodforinversedatadomainsurfacemultipleattenuationisbasedonasparsityconstraintandarankconstraintintheinverseoperatordesign.Thesparsityconstraintensuresthattheinverseoperatorhasasparserepresentationintheappropriatebasis,whiletherankconstraintensuresthattheinverseoperatorhasalowrank.Theseconstraintshelptopreventoverfittingandimprovethestabilityandaccuracyoftheinverseoperator.

Inaddition,weproposeanewmethodforselectingtheoptimalregularizationparameterbasedonthetruncatedsingularvaluedecompositionmethod.Thismethodinvolvescomputingthetruncatedsingularvaluedecompositionoftheforwardoperatormatrixandselectingtheoptimalregularizationparameterbasedonthesingularvalues.Thismethodiscomputationallyefficientandprovidesagoodestimateoftheoptimalregularizationparameter.

Results

Theperformanceoftheproposedmethodistestedonsyntheticandrealdata.Thesyntheticdataconsistsofasimplemodelwithareflectorandasurfacemultiple.Therealdataconsistsofafielddatasetwithcomplexgeologicalstructuresandstrongsurfacemultiples.

Theresultsdemonstratethattheproposedmethodiseffectiveinreducingsurfacemultiplesandpreservingprimaryreflections.Themethodisalsorobusttonoiseandcanhandlecomplexgeologicalstructures.Theresultsalsoshowthattheproposedmethodoutperformsconventionalmethodsintermsofsurfacemultipleattenuation.

Conclusion

Inthispaper,weproposedanewmethodforinversedatadomainsurfacemultipleattenuation.Theproposedmethodisbasedonasparsityconstraintandarankconstraintintheinverseoperatordesign.Wealsopresentedanewmethodforselectingtheoptimalregularizationparameterbasedonthetruncatedsingularvaluedecompositionmethod.Theperformanceoftheproposedmethodwastestedonsyntheticandrealdata,andtheresultsdemonstrateitseffectivenessinreducingsurfacemultiplesandpreservingprimaryreflections.Theproposedmethodrepresentsasignificantimprovementoverconventionalmethodsandhasthepotentialtoimprovethequalityofseismicdatainterpretation.Theproposedmethodforinversedatadomainsurfacemultipleattenuationhasseveraladvantagesoverconventionalmethods.Thesparsityconstraintandrankconstraintintheinverseoperatordesignpreventoverfittingandimprovethestabilityandaccuracyoftheinverseoperator.Additionally,theuseofthetruncatedsingularvaluedecompositionmethodforselectingtheoptimalregularizationparameteriscomputationallyefficientandprovidesagoodestimateoftheoptimalregularizationparameter.

Theperformanceoftheproposedmethodwastestedonbothsyntheticandrealdata.Thesyntheticdataconsistedofasimplemodelwithareflectorandasurfacemultiple,whiletherealdatawascollectedfromafielddatasetwithcomplexgeologicalstructuresandstrongsurfacemultiples.Theresultsdemonstratedthattheproposedmethodwaseffectiveinreducingsurfacemultiplesandpreservingprimaryreflections.Furthermore,theproposedmethodwasrobusttonoiseandcouldhandlecomplexgeologicalstructures.

Surfacemultipleattenuationisacriticalstepinseismicdataprocessingasitcansignificantlyimprovethequalityofseismicdatainterpretation.Theproposedmethodprovidesasignificantimprovementoverconventionalmethodsandhasthepotentialtomakeasignificantimpactinthefieldofseismicdataprocessing.Thereisstillroomforfurtherresearchanddevelopmentoftheproposedmethod,particularlyinaddressingthechallengesofhandlingnoiseandcomplexgeologicalstructures.However,theproposedmethodrepresentsapromisingapproachtosurfacemultipleattenuationinseismicdataprocessing.Furtherresearchontheproposedmethodcaninvolveinvestigatingitsperformanceondifferenttypesofdata,suchasdataacquiredwithdifferentacquisitiongeometriesandfrequencies.Additionally,themethodcanbeoptimizedtohandlemorecomplexandvariablesubsurfacestructures,whichcanposechallengestotraditionalmultiplesuppressiontechniques.

Anotherareaofresearchcanbeontheintegrationoftheproposedmethodwithotherseismicprocessingtechniquessuchasdeconvolutionandmigrationtoimprovethefinalimagequality.Furthermore,theproposedmethodcanbeextendedtoincludetheattenuationofinternalmultiples,whichcanresultinmoreaccuratesubsurfaceimagingandinterpretation.

Thecomputationalefficiencyoftheproposedmethodcanalsobeimprovedbyexploringparallelcomputingtechniques,whichcanspeeduptheprocessingtimeandincreasethepracticalityofthemethodforreal-timeseismicprocessingapplications.

Overall,theproposedmethodforinversedatadomainsurfacemultipleattenuationhasthepotentialtoimprovethequalityandreliabilityofseismicdatainterpretation.Withfurtherresearchanddevelopment,itcanbecomeavaluabletoolfortheoilandgasindustry,aidingintheexplorationandproductionofenergyresourceswhilereducingcostsandminimizingenvironmentaldamage.Anotherareaofpotentialresearchfortheproposedmethodisitsapplicabilitytounconventionalhydrocarbonresources,suchasshalegasandoil.Theseresourcespresentuniquechallengestotraditionalseismicdataprocessingtechniquesduetotheircomplexgeologicalstructuresandproductioncharacteristics.Byintegratingtheproposedmethodwithotherprocessingtechniques,suchaswaveforminversionandfullwaveforminversion,moreaccuratesubsurfaceimagingcanbeachievedfortheseresources,leadingtobetterexplorationandproductiondecisions.

Theproposedmethodcanalsobeextendedtohandleothertypesofsurface-relatedinterference,suchasgroundrollandcoherentnoise.Groundrollisatypeofsurfacewavethatcancompromisethequalityofsubsurfaceimages,whilecoherentnoisecanresultfromboreholeinteractions,suchastoolvibrationsandairblasts.Byincorporatingtheproposedmethodintoacomprehensiveseismicdataprocessingworkflowthataddressesalltypesofinterference,moreaccuratesubsurfaceimagescanbegenerated.

Furthermore,theproposedmethodcanbeappliedtootherfieldsbesidesoilandgasexploration,suchasenvironmentalandgeotechnicalstudies.Forexample,itcanbeusedtoimagesubsurfacestructuresanddetectpotentialhazards,suchassinkholes,landslides,andundergroundwate