一种非白噪反射系数序列的预测反褶积方法（英文）
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摘要
常规预测反褶积方法需要假设反射系数是不相关的白噪声序列,利用维纳-霍夫(WH,Wiener-Hopf)方程求解滤波器,消除地震记录中的相关成分,从而实现衰减多次波和提高分辨率的目的。实际上,一次波反射系数序列存在一定的相关成分,不满足白噪声假设,处理后反射系数序列的相关成分也被消除掉,导致有效信号失真。针对这一问题,本文提出了一种改进方法。首先,利用谱模拟方法直接从地震记录中估计子波自相关;其次,利用估引计的子波自相关构建包含多次波相关信息并避免一次波反射系数相关信息的自相关函数;最后,将构建的自相关函数带入WH方程,计算预测滤波算子进行预测反褶积处理。文中对该方法进行了模型试算和实际资料处理,并于与传统预测反褶积进行对比,结果表明:本文方法能够适应非白噪的反射系数序列,处理后不改变反射系数序列的统计特性,与传统预测反褶积相比,本方法在不降低多次波衰减能力和数据分辨率提升水平的前提下,大大降低了处理噪声,提高了处理的保真性。
Conventional predictive deconvolution assumes that the reflection coefficients of the earth conform to an uncorrelated white noise sequence. The Wiener-Hopf(WH) equation is constructed to solve the filter and eliminate the correlated components of the seismic records, attenuate multiples, and improve seismic resolution. However, in practice, the primary reflectivity series of field data rarely satisfy the white noise sequence assumption, with the result that the correlated components of the primary reflectivity series are also eliminated by traditional deconvolution. This results in signal distortion. To solve this problem, we have proposed an improved method for deconvolution. First, we estimated the wavelet correlation from seismic records using the spectrum-modeling method. Second, this wavelet autocorrelation was used to construct a new autocorrelation function which contains the correlated components caused by the existence of multiples and avoids the correlated components of the primary reflectivity series. Finally, the new autocorrelation function was brought into the WH equation, and the predictive filter operator was calculated for deconvolution. In this paper, we have applied this new method to simulated and field data processing, and we have compared its performance with that of traditional predictive deconvolution. Our results show that the new method can adapt to non-white reflectivity series without changing the statistical characteristics of the primary reflection coefficient series. Compared with traditional predictive deconvolution, the new method reduces processing noise and improves fidelity, all while maintaining the ability to attenuate multiples and enhance seismic resolution.
Conventional predictive deconvolution assumes that the reflection coefficients of the earth conform to an uncorrelated white noise sequence. The Wiener-Hopf(WH) equation is constructed to solve the filter and eliminate the correlated components of the seismic records, attenuate multiples, and improve seismic resolution. However, in practice, the primary reflectivity series of field data rarely satisfy the white noise sequence assumption, with the result that the correlated components of the primary reflectivity series are also eliminated by traditional deconvolution. This results in signal distortion. To solve this problem, we have proposed an improved method for deconvolution. First, we estimated the wavelet correlation from seismic records using the spectrum-modeling method. Second, this wavelet autocorrelation was used to construct a new autocorrelation function which contains the correlated components caused by the existence of multiples and avoids the correlated components of the primary reflectivity series. Finally, the new autocorrelation function was brought into the WH equation, and the predictive filter operator was calculated for deconvolution. In this paper, we have applied this new method to simulated and field data processing, and we have compared its performance with that of traditional predictive deconvolution. Our results show that the new method can adapt to non-white reflectivity series without changing the statistical characteristics of the primary reflection coefficient series. Compared with traditional predictive deconvolution, the new method reduces processing noise and improves fidelity, all while maintaining the ability to attenuate multiples and enhance seismic resolution.
引文
Broadhead,M.K.,Liner,C.L.,Ulrych,T.J.,et al.,2009,Predictive deconvolution by frequency domain wiener filtering:Journal of Seismic Exploration,18,347-356.
Carneiro,R.N.C.,Leite,L.W.B.,Vieira,W.W.S.,et al.,2017,Predictive deconvolution of multiple free surface in marine seismic data:International Congress of the Brazilian Geophysical Society,598-602.
Donno,D.,2011,Improving multiple removal using least-squares dip filters and independent component analysis:Geophysics,76(5),V91-V104.
Guitton,A.,and Verschuur,D.J.,2004,Adaptive subtraction of multiplesusing the L1-norm:Geophysical Prospecting,52(1),27-38.
Kazemeini,S.H.,Yang,C.,Juhlin,C.,et al.,2010,Enhancing seismic data resolution using the prestack blueing technique:An example from the Ketzin CO2injection site,Germany:Geophysics,75(6),V101-V110.
Lancaster,S.,and Whitcombe,D.,2000,Fast-track‘coloured’inversion:70th Annual International Meeting,SEG Expanded Abstracts,1572-1575.
Li,Z.X.,Li,Z.C.,and Lu W.K.,2016,Multichannel predictive deconvolution based on the fast iterative shrinkage-thresholding algorithm:Geophysics,81(1),V17-V30.
Liu,J.,and Lu,W.K.,2008,An improved predictive deconvolution based on maximization of nonGaussianity:Applied Geophysics,5(3),189-196.
Liu,L.,and Lu,W.K.,2014.Non-Gaussianity based time varying predictive deconvolution for multiple removal:SEG Technical Program Expanded,4162-4166.
Peacock,K.L.,and Treitel,S.,1969,Predictive deconvolution:Theory and practice:Geophysics,34(2),155-169.
Robinson E.A.,1957,Predictive decomposition of seismic traces:Geophysics,22(4),767-778.
Porsani,M.J.,and Ursin,B.,2007,Direct multichannel predictive deconvolution:Geophysics,2007,72(2),H11-H27.
Rosa,A.L.R.,and Ulrych T.J.,1991,Processing via spectral modeling:Geophysics,56(8),1244-1251.
Rosenberger,A.,Meyer,H.,and Buttkus,B.,1999,A multichannel approach to long-period multiple prediction and attenuation:Geophysical Prospecting,47(6),903-921.
Rietsch,E.,1983,What is the color of reflection coefficient series?:SEG Technical Program Expanded,442-444.
Saggaf,M.M.,and Robinson,E.A.,2000,A unified framework for the deconvolution of traces of nonwhite reflectivity:Geophysics,65(5),1660-1676.
Saggaf,M.M.,and Toksoz,M.N.,1999,An analysis of deconvolution:Modeling reflectivity by fractionally integrated noise:Geophysics,64(4),1093-1107.
Sinton,J.B.,Ward,R.W.,and Watkins,J.S.1978,Suppression of long-delay multiple reflections by predictive deconvolution:Geophysics,43(7),1352-1367.
Taner,M.T.,O’Doherty,R.F.,and Koehler,F.,1995,Long period multiple suppression by predictive deconvolution in the x-t domain:Geophysical Prospecting,43(4),433-468.
Todoeschuck,J.P.,and Jensen,O.G.,1988,Joseph geology and seismic deconvolution:Geophysics,53(11),1410-1414.
Todoeschuck,J.P.,and Jensen,O.G.,1989,Scaling geology and seismic deconvolution:Pure and Applied Geophysics,131(1-2),273-287.
Todoeschuck,J.P.,Jensen,O.G.,and Labonte,S.,1990,Gaussian scaling noise model of seismic reflection sequences:Evidence from well logs:Geophysics,55(4),480-484.
Ulrych,T.J.,1999,The whiteness hypothesis:Reflectivity,inversion,chaos,and Enders:Geophysics,64(5),1512-1523.
Ulrych,T.J.,and Matsuoka,T.,1991,The output of predictive deconvolution:Geophysics,56(3),371-377.
Ulrych,T.J.,and Sacchi,M.D.,2009,Resolution,a Gedanken tale:Sampling,Blueness and Noise:SEGTechnical Program Expanded,3083-3087.
Walden,A.T.,1985,Non-Gaussian reflectivity,entropy,and deconvolution:Geophysics,50(12),2862-2888.
Walden,A.T.,and Hosken,J.W.,J.1985,An investigation of the spectral properties of primary reflection coefficients:Geophysical Prospecting,33(3),400-435.
Wang,D.Y.,Huang,J.P.,Kong,X.,et al.,2017,Improving the resolution of seismic traces based on the secondary time-frequency spectrum:Applied Geophysics,14(2),236-246.
Wang,J.S.,and Cao,G.R.,1998,The method of fractal impulse deconvolution:Chinese Journal of Geophysics,41(1),99-108.
Wu,H.Z.,Fu,L.Y.,and Meng,X.H.,2007,Blind deconvolution of seismic signals with non-white reflectivities:Exploration Geophysics,38(4),235-241.
Yilmaz,O.,and Doherty,S.M.,2001,Seismic Data Analysis:Processing,Inversion,and Interpretation of Seismic Data:Society of Exploration Geophysicists,USA,159-269.
Carneiro,R.N.C.,Leite,L.W.B.,Vieira,W.W.S.,et al.,2017,Predictive deconvolution of multiple free surface in marine seismic data:International Congress of the Brazilian Geophysical Society,598-602.
Donno,D.,2011,Improving multiple removal using least-squares dip filters and independent component analysis:Geophysics,76(5),V91-V104.
Guitton,A.,and Verschuur,D.J.,2004,Adaptive subtraction of multiplesusing the L1-norm:Geophysical Prospecting,52(1),27-38.
Kazemeini,S.H.,Yang,C.,Juhlin,C.,et al.,2010,Enhancing seismic data resolution using the prestack blueing technique:An example from the Ketzin CO2injection site,Germany:Geophysics,75(6),V101-V110.
Lancaster,S.,and Whitcombe,D.,2000,Fast-track‘coloured’inversion:70th Annual International Meeting,SEG Expanded Abstracts,1572-1575.
Li,Z.X.,Li,Z.C.,and Lu W.K.,2016,Multichannel predictive deconvolution based on the fast iterative shrinkage-thresholding algorithm:Geophysics,81(1),V17-V30.
Liu,J.,and Lu,W.K.,2008,An improved predictive deconvolution based on maximization of nonGaussianity:Applied Geophysics,5(3),189-196.
Liu,L.,and Lu,W.K.,2014.Non-Gaussianity based time varying predictive deconvolution for multiple removal:SEG Technical Program Expanded,4162-4166.
Peacock,K.L.,and Treitel,S.,1969,Predictive deconvolution:Theory and practice:Geophysics,34(2),155-169.
Robinson E.A.,1957,Predictive decomposition of seismic traces:Geophysics,22(4),767-778.
Porsani,M.J.,and Ursin,B.,2007,Direct multichannel predictive deconvolution:Geophysics,2007,72(2),H11-H27.
Rosa,A.L.R.,and Ulrych T.J.,1991,Processing via spectral modeling:Geophysics,56(8),1244-1251.
Rosenberger,A.,Meyer,H.,and Buttkus,B.,1999,A multichannel approach to long-period multiple prediction and attenuation:Geophysical Prospecting,47(6),903-921.
Rietsch,E.,1983,What is the color of reflection coefficient series?:SEG Technical Program Expanded,442-444.
Saggaf,M.M.,and Robinson,E.A.,2000,A unified framework for the deconvolution of traces of nonwhite reflectivity:Geophysics,65(5),1660-1676.
Saggaf,M.M.,and Toksoz,M.N.,1999,An analysis of deconvolution:Modeling reflectivity by fractionally integrated noise:Geophysics,64(4),1093-1107.
Sinton,J.B.,Ward,R.W.,and Watkins,J.S.1978,Suppression of long-delay multiple reflections by predictive deconvolution:Geophysics,43(7),1352-1367.
Taner,M.T.,O’Doherty,R.F.,and Koehler,F.,1995,Long period multiple suppression by predictive deconvolution in the x-t domain:Geophysical Prospecting,43(4),433-468.
Todoeschuck,J.P.,and Jensen,O.G.,1988,Joseph geology and seismic deconvolution:Geophysics,53(11),1410-1414.
Todoeschuck,J.P.,and Jensen,O.G.,1989,Scaling geology and seismic deconvolution:Pure and Applied Geophysics,131(1-2),273-287.
Todoeschuck,J.P.,Jensen,O.G.,and Labonte,S.,1990,Gaussian scaling noise model of seismic reflection sequences:Evidence from well logs:Geophysics,55(4),480-484.
Ulrych,T.J.,1999,The whiteness hypothesis:Reflectivity,inversion,chaos,and Enders:Geophysics,64(5),1512-1523.
Ulrych,T.J.,and Matsuoka,T.,1991,The output of predictive deconvolution:Geophysics,56(3),371-377.
Ulrych,T.J.,and Sacchi,M.D.,2009,Resolution,a Gedanken tale:Sampling,Blueness and Noise:SEGTechnical Program Expanded,3083-3087.
Walden,A.T.,1985,Non-Gaussian reflectivity,entropy,and deconvolution:Geophysics,50(12),2862-2888.
Walden,A.T.,and Hosken,J.W.,J.1985,An investigation of the spectral properties of primary reflection coefficients:Geophysical Prospecting,33(3),400-435.
Wang,D.Y.,Huang,J.P.,Kong,X.,et al.,2017,Improving the resolution of seismic traces based on the secondary time-frequency spectrum:Applied Geophysics,14(2),236-246.
Wang,J.S.,and Cao,G.R.,1998,The method of fractal impulse deconvolution:Chinese Journal of Geophysics,41(1),99-108.
Wu,H.Z.,Fu,L.Y.,and Meng,X.H.,2007,Blind deconvolution of seismic signals with non-white reflectivities:Exploration Geophysics,38(4),235-241.
Yilmaz,O.,and Doherty,S.M.,2001,Seismic Data Analysis:Processing,Inversion,and Interpretation of Seismic Data:Society of Exploration Geophysicists,USA,159-269.