基于深度学习的无相位电磁反演方法研究

基于深度学习的无相位电磁反演方法研究基于深度学习的无相位电磁反演方法研究

摘要：电磁反演技术在资源勘探、地球内部结构探测与非破坏性检测等领域有着广泛的应用。无相位电磁反演方法是一种可以实现全角度反演的技术，具有非常好的稳定性和鲁棒性，但是其对相位的要求比较高，对数据采集条件有着较高的要求限制。基于深度学习的无相位电磁反演方法采用了深度神经网络来学习数据的复杂非线性映射关系，能够有效地解决相位信息缺失的问题，提高了反演方法的稳定性和可靠性。本文基于深度学习的无相位电磁反演方法进行了深入研究，设计了深度卷积神经网络模型，并通过模拟实验和实际数据反演应用进行了验证。结果表明，本文提出的方法在处理相位信息缺失的同时，不仅提高了反演的稳定性和可靠性，而且对数据采集条件的要求也得到了显著降低。

关键词：电磁反演；无相位反演；深度学习；深度卷积神经网络；稳定性

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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