基于混合时频分析技术的地震数据噪声压制(英文)
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摘要
针对复杂地质结构、陡倾角相干噪声、空间采样不均匀等情况下F-x域反褶积去噪技术的不足,提出首先应用具有时-频聚集性度量准则的广义S变换将时间-空间域的地震数据变换至时间-频率-空间域(t-f-x)的数据,在t-f-x域中对每一个频率切片应用经验模态分解(EMD),移除噪声占主导地位的本征模态函数以压制相干和随机噪声的滤波方法。模型分析表明第一本征模态函数表征的高频信息以噪声为主,移除第一本证模态函数可以达到压制噪声的目的。经广义S变换后形成t-f-x域中EMD滤波方法等效于具有依赖于空间位置、频率、高波数截断特征的自适应f-k滤波。此滤波方法考虑了数据的局部时-频特征,且具有执行简单的特点。与AR预测滤波方法比较,此法滤除的成分包含较少的低波数的信息,滤除的成分非常的局部化,且获得结果没有表现出过度平滑的特征。实际资料的应用表明在经广义S变换后形成t-f-x域中运用EMD滤波方法能够有效地压制随机和陡倾角相干噪声。
Deconvolution denoising in the f-x domain has some defects when facing situations like complicated geology structure, coherent noise of steep dip angles, and uneven spatial sampling. To solve these problems, a new filtering method is proposed, which uses the generalized S transform which has good time-frequency concentration criterion to transform seismic data from the time-space to time-frequency-space domain (t-f-x). Then in the t-f-x domain apply Empirical Mode Decomposition (EMD) on each frequency slice and clear the Intrinsic Mode Functions (IMFs) that noise dominates to suppress coherent and random noise. The model study shows that the high frequency component in the first IMF represents mainly noise, so clearing the first IMF can suppress noise. The EMD filtering method in the t-f-x domain after generalized S transform is equivalent to self-adaptive f-k filtering that depends on position, frequency, and truncation characteristics of high wave numbers. This filtering method takes local data time-frequency characteristic into consideration and is easy to perform. Compared with AR predictive filtering, the component that this method filters is highly localized and contains relatively fewer low wave numbers and the filter result does not show over-smoothing effects. Real data processing proves that the EMD filtering method in the t-f-x domain after generalized S transform can effectively suppress random and coherent noise of steep dips.
Deconvolution denoising in the f-x domain has some defects when facing situations like complicated geology structure, coherent noise of steep dip angles, and uneven spatial sampling. To solve these problems, a new filtering method is proposed, which uses the generalized S transform which has good time-frequency concentration criterion to transform seismic data from the time-space to time-frequency-space domain (t-f-x). Then in the t-f-x domain apply Empirical Mode Decomposition (EMD) on each frequency slice and clear the Intrinsic Mode Functions (IMFs) that noise dominates to suppress coherent and random noise. The model study shows that the high frequency component in the first IMF represents mainly noise, so clearing the first IMF can suppress noise. The EMD filtering method in the t-f-x domain after generalized S transform is equivalent to self-adaptive f-k filtering that depends on position, frequency, and truncation characteristics of high wave numbers. This filtering method takes local data time-frequency characteristic into consideration and is easy to perform. Compared with AR predictive filtering, the component that this method filters is highly localized and contains relatively fewer low wave numbers and the filter result does not show over-smoothing effects. Real data processing proves that the EMD filtering method in the t-f-x domain after generalized S transform can effectively suppress random and coherent noise of steep dips.
引文
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Hassan,H.,2005,Empirical mode decomposition EMD ofpotential field data:Airborne gravity data as an example:75th Ann.Internat.Mtg.,Soc.Expl.Geophys.,ExpandedAbstracts,704–708.
Huang,N.E.,Shen,Z.,Long,S.R.,Wu,M.L.,Shih,H.H.,Zheng,Q.,Yen,N.C.,Tung,C.C.,and Liu,H.H.,1998,The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis:Proceedings of the Royal Society of London SeriesA-Mathematical Physical and Engineering Sciences,454,903–995.
Jones,L.,and Parks,T.W.,1990,A high resolution data-adaptive time-frequency representation:IEEE Trans.Signal Process,38(12),2127–2135.
Rilling,G.,and Flandrin,P.,2009,Sampling effects on theempirical mode decomposition:Advanced Adaptive DataAnalysis,1,43–59.
Roohollah,A.and Siahkoohi,H.R.,2008,Ground rollattenuation using s and x-f-k transforms:GeophysicalProspecting,56,105–114.
Shen,H.Y.,Li,H.Y.,and Li,Q.C.,2010,SVD(singularvalue decomposition)seismic wave field noiseelimination in frequency domain:Oil GeophysicalProspecting,45(2),187–189.
Stockwell,R.G.,Mansinha,L.,and Lowe,R.P.,1996,Localization of the complex spectrum:The S transform:IEEE Transactions on Signal Processing,44(4),998–1001.
Battista,B.M.,Knapp,C.,McGee,T.,and Goebel,V.,2007,Application of the empirical mode decompositionand Hilbert-Huang transform to seismic reflection data:Geophysics,72(2),H29–H37.
Bekara,M.,and van der Baan,M.,2007,Local singularvalue decomposition for signal enhancement of seismicdata:Geophysics,72(2),V59–V65.
Bekara,M.,and van der Baan,M.,2009,Randomand coherent noise attenuation by empirical modedecomposition:Geophysics,74(5),V89–V97.
Canales,L.,1984,Random noise reduction:54th Ann.Internat.Mtg.,Soc.Expl.Geophys.,Expanded Abstracts,525–527.
Flandrin,P.,Rilling,G.,and Goncalves,P.,2005,Empiricalmode decomposition as a filter bank:IEEE SignalProcessing Letters,11,112–114.
Gabor,D.,1946,Theory of communication:Journal ofIEEE,93(3),429–457.
Galbraith,M.,1991,Random noise attenuation by f-xprediction:A tutorial:61th Ann.Internat.Mtg.,SocExpl.Geophys.,Expanded Abstracts,1428–1431.
Harris,P.E.,and White,R.E.,1997,Improving theperformance of f-x prediction at low signal-to-noiseratios:Geophysical Prospecting,45,269–302.
Hassan,H.,2005,Empirical mode decomposition EMD ofpotential field data:Airborne gravity data as an example:75th Ann.Internat.Mtg.,Soc.Expl.Geophys.,ExpandedAbstracts,704–708.
Huang,N.E.,Shen,Z.,Long,S.R.,Wu,M.L.,Shih,H.H.,Zheng,Q.,Yen,N.C.,Tung,C.C.,and Liu,H.H.,1998,The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis:Proceedings of the Royal Society of London SeriesA-Mathematical Physical and Engineering Sciences,454,903–995.
Jones,L.,and Parks,T.W.,1990,A high resolution data-adaptive time-frequency representation:IEEE Trans.Signal Process,38(12),2127–2135.
Rilling,G.,and Flandrin,P.,2009,Sampling effects on theempirical mode decomposition:Advanced Adaptive DataAnalysis,1,43–59.
Roohollah,A.and Siahkoohi,H.R.,2008,Ground rollattenuation using s and x-f-k transforms:GeophysicalProspecting,56,105–114.
Shen,H.Y.,Li,H.Y.,and Li,Q.C.,2010,SVD(singularvalue decomposition)seismic wave field noiseelimination in frequency domain:Oil GeophysicalProspecting,45(2),187–189.
Stockwell,R.G.,Mansinha,L.,and Lowe,R.P.,1996,Localization of the complex spectrum:The S transform:IEEE Transactions on Signal Processing,44(4),998–1001.
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