互补盲点策略和U型Transformer的地震数据去噪

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中图分类号：TP399 文献标志码：A 文章编号：1001-3695（2025）07-018-2056-08

doi： 10.19734/j. issn.1001-3695.2024.12.0516

Abstract：Randomnoisedenoisingcanefectivelyimprovesthesignal-to-noiseratio（SNR）ofseismicdata.Blindspot-driven unsuperviseddenoising methodsdonotrequirelabeleddataandcanautomaticallextractfeatures，buttheyignore noisecorrelations，leading tosuboptimalperformance.Toaddress thisisse，thispaperproposed thecomplementaryblindspotstrategy andU-shapedTransformerseismicdenoising framework（CBUTS）.Firstly，thecomplementaryblind-spotstrategyusedtrace maskingandrandommaskingforcomplementarysamplingtoefectivelyweakenthespatialconnectionsof noise.Secondly，visibleblindspotlossfunction integrateddenoisedresultsfromboth non-blindandblindspots，reducing informationlo.Finall， the Transformer-based U-shaped blindspotnetwork（STU-Net）enhancedthecaptureof globalandlocal features，further weakened thenoisecorrelations，andmore accuratelypredictedvalidsignals.Experimentalresultsshow that，compared to classicalandadvancedsupervisedand unsupervised methods，CBUTSachievesbeterperformance indenoising noiseand preserving thecontinuityofseismicevents.Analysisandcomparisonconfirmtheapplicabilityof the method toseismicdata denoising.

Keywords：seismic data denoising；unsupervised；blind spot strategy；Transformer

0引言

由于勘探环境和测量设备等因素的影响，地震数据在采集过程中不可避免地会引入随机噪声。（剩余15864字）