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基于SVM-CEEMDWT的大地电磁信噪分离方法（英文）.docx

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基于SVM-CEEMDWT的大地电磁信噪分离方法（英文） Title:EarthElectromagneticSignalsandNoiseSeparationMethodbasedonSVM-CEEMDWT Abstract: Withtheincreasingimportanceofelectromagnetic(EM)signalsinvariousgeophysicalstudies,theaccurateseparationofEMsignalsfromnoisehasbecomeacriticaltask.Inthispaper,weproposeanovelmethodbasedonSupportVectorMachines(SVM)andCompleteEnsembleEmpiricalModeDecompositionwithAdaptiveNoise(CEEMDWT)toeffectivelyseparateandenhanceEMsignals. 1.Introduction ThestudyofEMsignalshasgainedsignificantattentionduetoitsapplicationsinvariousfieldssuchasearthquakeprediction,undergroundimaging,andmineralexploration.However,thepresenceofnoiseinEMsignalscanaffecttheaccuracyandreliabilityofdataanalysis.Therefore,itisnecessarytodeveloprobustmethodstoeffectivelyseparateEMsignalsfromnoise. 2.Methods 2.1SupportVectorMachines(SVM) SVMisasupervisedmachinelearningalgorithmthathasbeenwidelyusedinclassificationandregressiontasks.Itworksbyfindingtheoptimalhyperplanethatmaximizesthemarginbetweendifferentclasses.Inthisstudy,SVMisemployedtoclassifyEMsignalsandnoise. 2.2CompleteEnsembleEmpiricalModeDecompositionwithAdaptiveNoise(CEEMDWT) CEEMDWTisanadvancedsignaldecompositiontechniquethatcombinesEnsembleEmpiricalModeDecomposition(EEMD)andDiscreteWaveletTransform(DWT).EEMDdecomposesthesignalintoIntrinsicModeFunctions(IMFs)representingdifferentfrequencycomponents,whileDWThelpstoidentifytheoptimalIMFthatcontainstheEMsignal. 3.ProposedMethodology TheproposedmethodcombinesSVMandCEEMDWTtoseparateEMsignalsfromnoise.Theworkflowisasfollows: (a)Preprocessing:Therawdataispreprocessedtoremoveanyartifactsoroutliers. (b)CEEMDWTDecomposition:ThepreprocesseddataisdecomposedintoIMFsusingCEEMDWT. (c)FeatureExtraction:Statisticalfeaturessuchasenergy,entropy,andcorrelationcoefficientsareextractedfromeachIMF. (d)TrainingSVM:TheextractedfeaturesareusedtotraintheSVMmodeltoclassifyIMFsaseitherEMsignalornoise. (e)Classification:ThetrainedSVMmodelisthenappliedtoclassifytheIMFsofthetestdataaseitherEMsignalornoise. (f)SignalR