基于曲波变换的叠后三维地震资料去噪处理研究
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摘要
基于曲波变换的数学原理,采用数值算法最新研究成果,设计并实现了二维曲波变换应用于叠后三维地震资料去噪处理的算法和流程。采用曲波变换对南海某工区叠后三维地震资料进行去噪处理结果表明,地震资料的信噪比有了明显的提高,去噪后的地震剖面反射波同相轴清晰、连续性好、保真保幅、方差体清楚,从而为该地区后续的层位和断层解释、地震异常体识别、油藏描述和地震反演提供了更为真实的地震资料。
Based on the mathematic principl of curvelet transform,a new algorithm and process to apply two dimension curvelet transform in denoising poststack 3D seismic data have been designed and realized by using the latest research results of numeric algorithm.The 3D seismic data denoised by curvlet transform in a block of the western South China sea shows a significant improvement in signal/noise ratio,clear reflection events,good continuity,preserved amplitude and fidelity and clear variance volumes,which has become a kind of more credible sesmic data for subsequent interpretation of intervals and faults,identification of abnormal seismic bodies,reservoir description and seismic inversion in the block.
Based on the mathematic principl of curvelet transform,a new algorithm and process to apply two dimension curvelet transform in denoising poststack 3D seismic data have been designed and realized by using the latest research results of numeric algorithm.The 3D seismic data denoised by curvlet transform in a block of the western South China sea shows a significant improvement in signal/noise ratio,clear reflection events,good continuity,preserved amplitude and fidelity and clear variance volumes,which has become a kind of more credible sesmic data for subsequent interpretation of intervals and faults,identification of abnormal seismic bodies,reservoir description and seismic inversion in the block.
引文
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[9]张军华,梁晓腾,等.Curvelet变换及其在地震资料去噪中的应用[C].CPS/SEG Beijing2009,International GeophysicalConference&Exposition.1151.
[10]MALLAT S G.A wavelet tour of signal processing[M].Aca-demic Press,1997.
[11]STARCK J L,CANDES E J,DONOHO D L.The curvelettransform for i mage denoising[M].IEEE Transactions onI mage Processing,2002.
[12]YI NG,CANDES.3D discrete curvelet transform[C]//Wave-let XI.SPIE,2005.
[13]BROADHEAD M K.The i mpact of random noise on seismicwavelet esti mation[J].The Leading Edge,2008,(2):226-230.
[2]CANDES,DONOHO.Curvelets—a surprisingly effective non-adaptive representation for objects with edges[C]//Curvesand Surfaces.Nashville:Vanderbilt University Press,1999.
[3]HERRMANN F J,CRISTALL J.Curvelet denoising of4dseismic denoise[J].Expanded Abstracts,SEG,2004.
[4]HERRMANN F J,VERSCHUUR E.Separation of pri mariesand multiples by non-linear esti mationin the curvelet domain[J].Expanded Abstracts,SEG,2004.
[5]CARSON D T Y,HERRMANN F J.Curvelet processing andi maging:adaptive ground roll removal[J].Expanded Ab-stracts,SEG,2004.
[6]CANDES.Fast discrete curvelet transforms[J].MultiscaleModeling and Si mulation.2005,5:861-899.
[7]DEMANET L,YI NG Lexing.Curvelet and wave atoms formirror-extendedi mages[OL].(2007-07)[2009-03-02]http://www.curvelet.org.
[8]彭才,常智,朱仕军.基于曲波变换的地震数据去噪方法[J].石油物探.2008,47(5):461-464.
[9]张军华,梁晓腾,等.Curvelet变换及其在地震资料去噪中的应用[C].CPS/SEG Beijing2009,International GeophysicalConference&Exposition.1151.
[10]MALLAT S G.A wavelet tour of signal processing[M].Aca-demic Press,1997.
[11]STARCK J L,CANDES E J,DONOHO D L.The curvelettransform for i mage denoising[M].IEEE Transactions onI mage Processing,2002.
[12]YI NG,CANDES.3D discrete curvelet transform[C]//Wave-let XI.SPIE,2005.
[13]BROADHEAD M K.The i mpact of random noise on seismicwavelet esti mation[J].The Leading Edge,2008,(2):226-230.
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