基于峭度最大化的地震盲反褶积方法
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摘要
从地震褶积与反褶积过程的等效总系统出发,使脉冲响应满足单位脉冲序列形式,导出方差约束下反褶积输出的峭度最大化准则,作为地震盲反褶积的目标函数;针对目标函数关于反褶积算子的优化问题,采用粒子群算法进行全局寻优操作,实现地震信号的反褶积。数值模拟和实际资料处理结果表明,该方法不但适用于最小相位子波的反褶积,而且适用于混合相位子波的反褶积。与梯度法优化峭度准则的反褶积结果相比,能够更好地从地震记录中估计反射系数,拓宽地震资料的频谱,提高地震资料的分辨率。
From the perspective of the equivalent overall system generated from the seismic convolution and deconvolution,a maximum kurtosis criterion of deconvolution output with the constraint of variance is derived as objective function of seismic blind deconvolution by making the impulse response in the form of unit impulse sequence.Aiming at optimizing the deconvolution operator of objective function,Particle Swarm Optimization(PSO)was applied to proceed a global search for deconvolution operator in order to realize deconvolution of seismic data.The results of numerical simulation and actual seismic data processing prove that,the method proposed in this paper can be suitable for minimum phase wavelet deconvolution and for mixed phase wavelet deconvolution.In contrast to the deconvolution results of gradient method with optimizing kurtosis criterion,the proposed method can obtain a better estimation of reflectivity from the seismic data,and broaden the spectrum of seismic data more effectively resulting in higher resolution of seismic data.
From the perspective of the equivalent overall system generated from the seismic convolution and deconvolution,a maximum kurtosis criterion of deconvolution output with the constraint of variance is derived as objective function of seismic blind deconvolution by making the impulse response in the form of unit impulse sequence.Aiming at optimizing the deconvolution operator of objective function,Particle Swarm Optimization(PSO)was applied to proceed a global search for deconvolution operator in order to realize deconvolution of seismic data.The results of numerical simulation and actual seismic data processing prove that,the method proposed in this paper can be suitable for minimum phase wavelet deconvolution and for mixed phase wavelet deconvolution.In contrast to the deconvolution results of gradient method with optimizing kurtosis criterion,the proposed method can obtain a better estimation of reflectivity from the seismic data,and broaden the spectrum of seismic data more effectively resulting in higher resolution of seismic data.
引文
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[4]刘喜武,宁俊瑞,张改兰.Cauchy稀疏约束Bayesian估计地震盲反褶积框架与算法研究[J].石油物探,2009,48(5):459-464Liu X W,Ning J R,Zhang G L.Cauchy sparse con-strained Bayesian estimation based seismic blind de-convolution frame and algorithm[J].GeophysicalProspecting for Petroleum,2009,48(5):459-464
[5]Larue A,Mars J I,Jutten C.Frequency-domain blinddeconvolution based on mutual information rate[J].IEEE Transactions on Signal Processing,2006,54(5):1771-1780
[6]印兴耀,刘杰,杨培杰.一种基于负熵的Bussgang地震盲反褶积方法[J].石油地球物理勘探,2007,42(5):499-505Yin X Y,Liu J,Yang P J.A Bussgang seismic blinddeconvolution method based on negentropy[J].OilGeophysical Prospecting,2007,42(5):499-505
[7]Hani A F M,Younis M S.A HOS-based blind de-convolution algorithm for the improvement of timeresolution of mixed phase low SNR seismic data[J].Journal of Geophysics and Engineering,2009,6(1):87-100
[8]王锐,孙成禹.有限熵反褶积[J].石油地球物理勘探,2005,40(6):638-641Wang R,Sun C Y.Finite entropy deconvolution[J].Oil Geophysical Prospecting,2005,40(6):638-641
[9]Baan M V D,Fomel S.Nonstationary phase estima-tion using regularized local kurtosis maximization[J].Geophysics,2009,74(6):75-80
[10]Baan M V D.Time-varying wavelet estimation anddeconvolution by kurtosis maximization[J].Geo-physics,2008,73(2):11-18
[11]Shalvi O,Weistein E.New criteria for blind deconvo-lution of non-minimum phase system[J].IEEETransactions on Information Theory,1990,36(2):312-321
[12]Kennedy J,Eberhart R C.Particle swarm optimiza-tion[J].Proceedings of the IEEE International Con-ference on Neural Networks,1999,10(3):626-634
[13]Trelea I C.The particle swarm optimization algo-rithm:convergence analysis and parameter selection[J].Information Processing Letters,2003,85(6):317-325
[2]Canadas G.A mathematical framework for blind de-convolution inverse problems[J].Expanded Ab-stracts of 72nd Annual Internat SEG Mtg,2002,2202-2205
[3]张繁昌,刘杰,印兴耀.修正柯西约束地震盲反褶积方法[J].石油地球物理勘探,2008,43(4):391-396Zhang F C,Liu J,Yin X Y.Modified Cauchy-con-strained seismic blind deconvolution[J].Oil Geo-physical Prospecting,2008,43(4):391-396
[4]刘喜武,宁俊瑞,张改兰.Cauchy稀疏约束Bayesian估计地震盲反褶积框架与算法研究[J].石油物探,2009,48(5):459-464Liu X W,Ning J R,Zhang G L.Cauchy sparse con-strained Bayesian estimation based seismic blind de-convolution frame and algorithm[J].GeophysicalProspecting for Petroleum,2009,48(5):459-464
[5]Larue A,Mars J I,Jutten C.Frequency-domain blinddeconvolution based on mutual information rate[J].IEEE Transactions on Signal Processing,2006,54(5):1771-1780
[6]印兴耀,刘杰,杨培杰.一种基于负熵的Bussgang地震盲反褶积方法[J].石油地球物理勘探,2007,42(5):499-505Yin X Y,Liu J,Yang P J.A Bussgang seismic blinddeconvolution method based on negentropy[J].OilGeophysical Prospecting,2007,42(5):499-505
[7]Hani A F M,Younis M S.A HOS-based blind de-convolution algorithm for the improvement of timeresolution of mixed phase low SNR seismic data[J].Journal of Geophysics and Engineering,2009,6(1):87-100
[8]王锐,孙成禹.有限熵反褶积[J].石油地球物理勘探,2005,40(6):638-641Wang R,Sun C Y.Finite entropy deconvolution[J].Oil Geophysical Prospecting,2005,40(6):638-641
[9]Baan M V D,Fomel S.Nonstationary phase estima-tion using regularized local kurtosis maximization[J].Geophysics,2009,74(6):75-80
[10]Baan M V D.Time-varying wavelet estimation anddeconvolution by kurtosis maximization[J].Geo-physics,2008,73(2):11-18
[11]Shalvi O,Weistein E.New criteria for blind deconvo-lution of non-minimum phase system[J].IEEETransactions on Information Theory,1990,36(2):312-321
[12]Kennedy J,Eberhart R C.Particle swarm optimiza-tion[J].Proceedings of the IEEE International Con-ference on Neural Networks,1999,10(3):626-634
[13]Trelea I C.The particle swarm optimization algo-rithm:convergence analysis and parameter selection[J].Information Processing Letters,2003,85(6):317-325
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