Simultaneous Denoising and Interpolation of Seismic Data via the Deep Learning Method

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    摘要 Utilizingdatafromcontrolledseismicsourcestoimagethesubsurfacestructuresandinvertthephysicalpropertiesofthesubsurfacemediaisamajoreffortinexplorationgeophysics.Denseseismicrecordswithhighsignal-to-noiseratio(SNR)andhighfidelityhelpsinproducinghighqualityimagingresults.Therefore,seismicdatadenoisingandmissingtracesreconstructionaresignificantforseismicdataprocessing.Traditionaldenoisingandinterpolationmethodsrarelyoccasionedrelyonnoiselevelestimations,thusrequiringheavymanualworktodealwithrecordsandtheselectionofoptimalparameters.Weproposeasimultaneousdenoisingandinterpolationmethodbasedondeeplearning.Fornoisyrecordswithmissingtraces,weadoptaniterativealternatingoptimizationstrategyandseparatetheobjectivefunctionofthedatarestoringproblemintotwosub-problems.Theseismicrecordscanbereconstructedbysolvingaleast-squareproblemandapplyingasetofpre-traineddenoisingmodelsalternativelyanditeratively.Wedemonstratethismethodwithsyntheticandfielddata.
    机构地区 不详
    出版日期 2019年01月11日(中国Betway体育网页登陆平台首次上网日期,不代表论文的发表时间)
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