基于深度學(xué)習(xí)的無(wú)相位電磁反演方法研究

基于深度學(xué)習(xí)的無(wú)相位電磁反演方法研究基于深度學(xué)習(xí)的無(wú)相位電磁反演方法研究

摘要：電磁反演技術(shù)在資源勘探、地球內(nèi)部結(jié)構(gòu)探測(cè)與非破壞性檢測(cè)等領(lǐng)域有著廣泛的應(yīng)用。無(wú)相位電磁反演方法是一種可以實(shí)現(xiàn)全角度反演的技術(shù)，具有非常好的穩(wěn)定性和魯棒性，但是其對(duì)相位的要求比較高，對(duì)數(shù)據(jù)采集條件有著較高的要求限制?；谏疃葘W(xué)習(xí)的無(wú)相位電磁反演方法采用了深度神經(jīng)網(wǎng)絡(luò)來(lái)學(xué)習(xí)數(shù)據(jù)的復(fù)雜非線性映射關(guān)系，能夠有效地解決相位信息缺失的問(wèn)題，提高了反演方法的穩(wěn)定性和可靠性。本文基于深度學(xué)習(xí)的無(wú)相位電磁反演方法進(jìn)行了深入研究，設(shè)計(jì)了深度卷積神經(jīng)網(wǎng)絡(luò)模型，并通過(guò)模擬實(shí)驗(yàn)和實(shí)際數(shù)據(jù)反演應(yīng)用進(jìn)行了驗(yàn)證。結(jié)果表明，本文提出的方法在處理相位信息缺失的同時(shí)，不僅提高了反演的穩(wěn)定性和可靠性，而且對(duì)數(shù)據(jù)采集條件的要求也得到了顯著降低。

關(guān)鍵詞：電磁反演；無(wú)相位反演；深度學(xué)習(xí)；深度卷積神經(jīng)網(wǎng)絡(luò)；穩(wěn)定性

Abstract:Electromagneticinversiontechnologyhasbeenwidelyusedinresourceexploration,Earth'sinteriorstructuredetectionandnon-destructivetesting.Phaselesselectromagneticinversionmethodisatechnologythatcanachievefull-angleinversion,andhasexcellentstabilityandrobustness,butithashighrequirementsforphaseanddataacquisitionconditions.Basedondeeplearning,thephaselesselectromagneticinversionmethodusesdeepneuralnetworkstolearnthecomplexnonlinearmappingrelationshipofdata,whichcaneffectivelysolvetheproblemofphaseinformationlossandimprovethestabilityandreliabilityoftheinversionmethod.Inthispaper,thephaselesselectromagneticinversionmethodbasedondeeplearningisdeeplystudied,andadeepconvolutionalneuralnetworkmodelisdesigned,whichisverifiedthroughsimulationexperimentsandactualdatainversionapplications.Theresultsshowthattheproposedmethodnotonlyimprovesthestabilityandreliabilityoftheinversionwhiledealingwithphaseinformationloss,butalsosignificantlyreducestherequirementsfordataacquisitionconditions.

Keywords:electromagneticinversion;phaselessinversion;deeplearning;deepconvolutionalneuralnetwork;stabilitElectromagneticinversionisanimportanttoolforgeophysicalexploration,whichaimstorecoverthesubsurfacephysicalpropertiesbasedonthemeasuredelectromagneticfields.However,inmanypracticalsituations,onlytheamplitudeoftheelectromagneticfieldscanbemeasured,whilethephaseinformationislost.Thisso-calledphaselessinversionproblemisill-posedandchallengingtosolve,whichgreatlylimitstheaccuracyandapplicabilityofelectromagneticinversion.

Totacklethisproblem,adeepconvolutionalneuralnetwork(CNN)modelisproposedinthisstudy.CNNisapowerfuldeeplearningtechniquethatcanautomaticallylearncomplexfeaturerepresentationsfrominputdata,whichhasshownremarkablesuccessinvariousimageandsignalprocessingtasks.TheproposedCNNmodelisspecificallydesignedforphaselesselectromagneticinversion,whichtakestheamplitudeofthemeasuredelectromagneticfieldsasinputandoutputsthecorrespondingsubsurfacephysicalproperties.

TheproposedCNNmodelistrainedusingsimulateddatawithknowngroundtruth,andthenverifiedthroughbothsimulationexperimentsandactualdatainversionapplications.Theresultsshowthattheproposedmethodcansignificantlyimprovethestabilityandreliabilityoftheinversionwhiledealingwithphaseinformationloss,andcanachievehighaccuracyandrobustnessevenundernoisyandincompletedata.Moreover,theproposedmethodcangreatlyreducetherequirementsfordataacquisitionconditions,whichcansavetimeandcostinpracticalapplications.

Insummary,theproposeddeepCNNmodelprovidesapromisingsolutiontothephaselesselectromagneticinversionproblem,whichcangreatlyenhancetheaccuracyandapplicabilityofgeophysicalexploration.FutureresearchcanfurtherexplorethepotentialofdeeplearningtechniquesinelectromagneticinversionandotherrelatedfieldsFutureresearchinthefieldofelectromagneticinversioncanfocusonseveralareasthathavethepotentialtoimprovetheaccuracyandefficiencyoftheproposeddeeplearningmethod.OnepossibledirectionistoincorporatemorecomplexmodelingtechniquesandadvancedalgorithmstofurtheroptimizetheperformanceoftheCNNmodel.Forinstance,theuseofdifferentactivationfunctionsorlossfunctionsmayleadtobetterresultsinsomecases.Additionally,theincorporationofmorepriorinformationorconstraints,suchasthesmoothnessorsparsityofthesolution,canbeexplored.

Anotherareaworthexploringistheapplicationoftheproposedmethodtoothergeophysicalexplorationtechniques,suchasseismicandgravitysurveys.Whilethefocusofthisstudywasontheelectromagneticinversionproblem,thedeepCNNapproachcanbeadaptedtoothergeophysicalfieldswithphaselessinversionproblems.Additionally,theproposedmethodcanbeappliedtoreal-worlddatasetstovalidateitseffectivenessinpracticalapplications.

Furthermore,thedevelopmentofhardwareandsoftwareinfrastructuretosupportdeeplearningalgorithmscanalsofacilitatetheuseoftheproposedmethodinpractice.Specifically,theuseofhigh-performancecomputingsystemsandparallelprocessingtechniquescangreatlyacceleratethecomputationaltimerequiredfortheCNNmodel.Additionally,thedevelopmentofuser-friendlysoftwareinterfacescanenablenon-expertstoapplythedeepCNNmethodtotheirowngeophysicaldatasets.

Finally,theintegrationoftheproposeddeeplearningmethodwithotherexplorationtools,suchastraditionalinversionmethodsorforwardmodelingtechniques,canprovideamorecomprehensiveandaccuratesolutiontogeophysicalexplorationproblems.Thecombinationofdifferentmethodscanexploitthestrengthsofeachapproachandovercomethelimitationsofindividualmethods.Therefore,futureresearchcaninvestigatethepotentialofcombiningdeeplearningwithothergeophysicalexplorationmethodstoimprovetheaccuracyandefficiencyoftheinversionprocess.

Inconclusion,theproposeddeepCNNmethodrepresentsapromisingapproachtosolvingthephaselesselectromagneticinversionproblemingeophysicalexploration.Thedevelopmentofmoreadvanceddeeplearningtechniques,aswellastheirintegrationwithotherexplorationmethods,canfurtherenhancetheaccuracyandpracticalapplicabilityofthemethod.Overall,theemergingfieldofdeeplearninghasthepotentialtorevolutionizegeophysicalexplorationandbenefitscientificresearchandindustrypracticesinmanywaysDeeplearninghasshowngreatpotentialinmanyfields,includinggeophysicalexploration.Oneofthemajoradvantagesofdeeplearningisitsabilitytolearncomplexpatternsandfeaturesfromlargedatasets.Thiscanbeparticularlyusefulingeophysicalexploration,wheretheinterpretationofdataishighlydependentontheexpertiseandexperienceoftheinterpreter.

Oneofthechallengesingeophysicalexplorationistheinversionproblem,wherethegoalistorecoverthesubsurfacepropertiesfromtheobservedgeophysicaldata.Thephaselesselectromagneticinversionproblemisaparticularinstanceofthisproblem,whereonlytheamplitudeofthescatteredelectromagneticfieldcanbemeasured,andthephaseinformationislost.Thisproblemcanbedifficulttosolve,andtraditionalinversionmethodscanbecomputationallyexpensiveandmaynotalwaysproducereliableresults.

Thehodmethodhasshownpromiseinsolvingthephaselesselectromagneticinversionproblem.Themethodusesadeepneuralnetworktopredictthephaseofthescatteredfieldgiventheamplitudeofthefieldandthesubsurfaceparameters.Thenetworkistrainedusingalargedatasetofsyntheticdata,andtheaccuracyoftheinversionisevaluatedusingaseparatetestdataset.

Thehodmethodhasseveraladvantagesovertraditionalinversionmethods.First,itcanbemuchfasterthantraditionalmethods,astheinversioncanbeperformedinamatterofsecondsonatypicalcomputer.Second,themethodishighlyscalable,asitcanbeappliedtolargedatasetsandcaneasilyincorporateadditionaldatasources,suchasseismicorwelldata.Finally,themethodishighlyinterpretable,astheneuralnetworkcanprovideinsightsintothesubsurfacepropertiesandtherelationshipbetweenthedataandthemodelparameters.

However,therearealsochallengesassociatedwiththehodmethod.Oneofthemainchallengesistheneedforlargeamountsoftrainingdata.Theneuralnetworkrequiresalargeamountofsyntheticdatatolearntherelationshipbetweentheamplitudeofthefieldandthesubsurfaceparameters.Thisdatacanbegeneratedusingnumericalsimulations,butthesimulationscanbecomputationallyexpensiveandtime-consuming.

Anotherchallengeisthepotentialforoverfitting.Theneuralnetworkcaneasilymemorizethetrainingdataandproduceoverlycomplexmodelsthatdonotgeneralizewelltonewdata.Toaddressthischallenge,techniquessuchasregularizationandcross-validationcanbeusedtoensurethatthemodelisnotoverfittingthetrainingdata.

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