地震波高频信息在薄层砂体预测中的应用
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摘要
为了证实地震波通频带以外高频信息在砂岩体预测中的有效性和应用价值,利用褶积模型分析了地震波的高频信息是精细描述砂岩地质体空间结构展布的基础;提出在最小限度地影响有效信号的条件下,利用Cur-velet变换进行地震数据噪声压制处理是地震资料处理流程中保留高频信息的关键环节和有效方法;采用具有自动调节时频聚集性能的自适应S变换,对经Curvelet变换噪声压制后的地震资料进行瞬时谱分解,可以获取高质量通频带以外与砂体厚度有密切关系的高频数据体,用于薄层砂岩体的精细预测。TH地区实际资料的处理结果表明,通频带以外高频端信息清晰地刻画了薄层砂体的边界和空间展布。
In order to confirm the effectiveness and application value of high-frequency information outside of seismic wave pass band for sand body prediction,the convolution model was used to verify that seismic wave high-frequency is the basis of fine delineation of geological body spatial structure for sand body.De-noising processing is the key of retaining high-frequency information in the course of seismic data processing flow.We put forward the methods,which on the minimal impacting effective signal conditions,utilizing Curvelet transform that is characteristic by minimal overlap in Curvelet transform domain for effective signal and noise,attenuate noise and retain effective high-frequency components of seismic wave,and using the adjustable flexible and aggregation time-frequency properties of self-adapting S transform(SAST) to extract high-frequency information,obtaining high-quality single-frequency data for sand body prediction.The processing result on actual data in TH area showed along horizon lateral iso-frequency slices of high-frequency.Meanwhile,outside the pass band,the spatial distribution and boundary of the sand body was clearly depicted.
In order to confirm the effectiveness and application value of high-frequency information outside of seismic wave pass band for sand body prediction,the convolution model was used to verify that seismic wave high-frequency is the basis of fine delineation of geological body spatial structure for sand body.De-noising processing is the key of retaining high-frequency information in the course of seismic data processing flow.We put forward the methods,which on the minimal impacting effective signal conditions,utilizing Curvelet transform that is characteristic by minimal overlap in Curvelet transform domain for effective signal and noise,attenuate noise and retain effective high-frequency components of seismic wave,and using the adjustable flexible and aggregation time-frequency properties of self-adapting S transform(SAST) to extract high-frequency information,obtaining high-quality single-frequency data for sand body prediction.The processing result on actual data in TH area showed along horizon lateral iso-frequency slices of high-frequency.Meanwhile,outside the pass band,the spatial distribution and boundary of the sand body was clearly depicted.
引文
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[17]Jones D L,Parks T W.A high resolution data-adap-tive time-frequency representation[J].IEEE Trans.Signal Process,1990,38(12):2127-2135
[2]王西文.提取地震信号高频信息方法的若干问题[J].石油地球物理勘探,2006,41(1):67-75Wang X W.Several issues of methods for detectinghigh-frequency information of seismic signal[J].OilGeophysical Prospecting,2006,41(1):67-75
[3]Liu J L,Marfurt K J.Instantaneous spectral attrib-utes to detect channels[J].Geophysics,2007,72(2):23-29
[4]Castagna J P,Sun S J,Siegfried R W.Instantaneousspectral analysis:Detection of low-frequency shad-ows associated with hydrocarbons[J].The LeadingEdge,2003,22(2):120-127
[5]Goloshubin G,van Schuyver C.Reservior imaging u-sing low frequencies of seismic reflections[J].TheLeading Edge,2006,25(5):527-531
[6]Beatriz Q,Stefan M S,Yuri Y P.Low-frequency re-flections from a thin layer with high attenuationcaused by interlayer flow[J].Geophysics,2009,74(15):15-23
[7]Candes E J,Donoho L D.Ridgelets:a key to higher-dimensional intermittency[J].Philosophical Traus-actions of the Royal Souety A 1999,357(3):2495-2509
[8]Candes E J,Donoho D L.Fast discrete curvelettransform[J].Multiscale Modeling&Simulation,2006,5(4):861-899
[9]Jean-Lue Starck Candes E J,Donoho D L.The cur-velet transform for image denoising[J].IEEE Transon Image Processing,2002,11(6):670-684
[10]Candes E J,Donolo D L.Continuous curvelet trans-form[J].ApZplied Computational Harmon Analy-sis,2005,19(2):198-222
[11]Herrmann F J,Wang D L,Hennenfent G,et al.Cur-velet-based seismic data processing:A multiscale andnonlinear approach[J].Geophysics,2008,73(1):1-4
[12]Ramesh N,Anatoly I B,Dominique G G,et al.Co-herent and random noise attenuation using the cur-velet transform[J].The Leading Edge,2008,27(2):240-248
[13]周金保.沉积微相研究成果在濮城油气田滚动勘探中的应用[J].石油勘探与开发,2004,31(4):68-70Zhou J B.Application of the micro-facies analysis inthe progressive exploration of Pucheng oil and gasfield[J].Petroleum Exploration and Development,2004,31(4):68-70
[14]Stockwell R G,Mansinha L,Lowe R P.Localizationof the complex spectrum:the S transform[J].IEEETransactions on Signal Processing,1996,44(4):998-1001
[15]高静怀,陈文超,李幼铭,等.广义S变换与薄互层地震响应分析[J].地球物理学报,2003,46(4):526-531Gao J H,Chen W C,Li Y M,et al.Generalized Stransform and seismic response analysis of thin in-terbeds[J].Chinese Journal of Geophysics,2003,46(4):526-531
[16]陈学华,贺振华.改进的S变换及在地震信号处理中的应用[J].数据采集与处理,2005,20(4):449-453.Chen X H,He Z H.Improved S-transform and ttsapplication in seismic signal processing[J].Journalof Data Acquisition&Processing,2005,20(4):449-453
[17]Jones D L,Parks T W.A high resolution data-adap-tive time-frequency representation[J].IEEE Trans.Signal Process,1990,38(12):2127-2135
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