基于高阶累积量的非最小相位地震子波提取
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摘要
地震子波的最小相位（MP）假设为不适定的反褶积问题的求解提供了前提，但是这个假设的正确性一直是值得怀疑的。长期以来，有关的研究多局限于利用地震记录的二阶统计信息即记录的自相关函授或功率谱来研究此问题。但由于二阶统计量并不包含相位信息，因而无法解决非最小相让（NMP）的物理问题。随着高阶累积量与多谱概念的引入，使得借助于高阶累积量或多谱乃至它们的切片（或投影）所携带的相让信息来识别NMP地震子波成为可能。为此，本文提出运用基于高阶累积量的MA参数估计技术提取非最小相位地震子波的方法。该方法充分利用了地震记录高阶累积量中的相位信息，从而避免了单纯依赖于优化技术而忽略物理实质的倾向。实际资料处理的结果表明，该方法在混合相位、非高斯信号的处理中具有明显的优势。
The minimum phase presumption of seismic wavelet offers a premise for us to solve ill-posed deconvolution problem; however, its correctness is doubtful. Most geophysicists have been analysing the problem only by using second-order statistical informations of seismic data, namely autocorrelation function or power spectrum of seismic data. Second-order statistics have no phase informations, so that they will not be used to solve physical problem of non-minimum phase.The introduction of high-order accumulated amount and multispectral conception makes it possible to identify non-minimum phase wavelet by using the phase informations of high-order accumulated amount or multispectra, or their slices (or projections ). A method for obtaining non-minimum phase wavelet by taking a moving-average parameter estimation based on high-order accumulated amount is recommended in this article. This method makes full use of phase information in seismic accumulated high-order amount, so that the neglect of physical essential which results from the only dependence of optimizing thecnique is avoided satisfactorily.Processing result of real data shows that the method is obviously superior in the processing of mixed-phase and non-Gaussian signals.
The minimum phase presumption of seismic wavelet offers a premise for us to solve ill-posed deconvolution problem; however, its correctness is doubtful. Most geophysicists have been analysing the problem only by using second-order statistical informations of seismic data, namely autocorrelation function or power spectrum of seismic data. Second-order statistics have no phase informations, so that they will not be used to solve physical problem of non-minimum phase.The introduction of high-order accumulated amount and multispectral conception makes it possible to identify non-minimum phase wavelet by using the phase informations of high-order accumulated amount or multispectra, or their slices (or projections ). A method for obtaining non-minimum phase wavelet by taking a moving-average parameter estimation based on high-order accumulated amount is recommended in this article. This method makes full use of phase information in seismic accumulated high-order amount, so that the neglect of physical essential which results from the only dependence of optimizing thecnique is avoided satisfactorily.Processing result of real data shows that the method is obviously superior in the processing of mixed-phase and non-Gaussian signals.
引文
1 Giannakis D.Identification of nonminimum phase system using higher order statistics. IEEE Trans on ASSP,1989,37(3)
2 Tugait J K. Mentification of linear system via second-and fourth-order cumulant matching.IEEE Transon Information Theory,1987,33(3)
3 Zhang X D and Zhang Y S. FIR system identification using higher order cumulants alone. IEEE Trans onSignal Processing,1994,42(10);2854～2858
4 张贤达.时间序列分析──高阶统计量方法,清华大学出版社,1996
5 Giannakis G and Mendel J M.Tomographic wavelet estimation via higher order statistics.In Proc 56th IntConf Soc Explore Geophys,Houston,TX,1986
6 Mendel J M.Tutorial on high order statistics (spectra) in signal processing and system theory:Theoreti-cal results and some application.Proc,IEEE,1991,79:278～305
7 Zhou T G et al. A hybrid second-and higher-order statistical method for blind deconvolution of seismicdata,Proceedings of IGSP’98,1998
2 Tugait J K. Mentification of linear system via second-and fourth-order cumulant matching.IEEE Transon Information Theory,1987,33(3)
3 Zhang X D and Zhang Y S. FIR system identification using higher order cumulants alone. IEEE Trans onSignal Processing,1994,42(10);2854～2858
4 张贤达.时间序列分析──高阶统计量方法,清华大学出版社,1996
5 Giannakis G and Mendel J M.Tomographic wavelet estimation via higher order statistics.In Proc 56th IntConf Soc Explore Geophys,Houston,TX,1986
6 Mendel J M.Tutorial on high order statistics (spectra) in signal processing and system theory:Theoreti-cal results and some application.Proc,IEEE,1991,79:278～305
7 Zhou T G et al. A hybrid second-and higher-order statistical method for blind deconvolution of seismicdata,Proceedings of IGSP’98,1998
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