三谱混合相位子波估计与最大后验反褶积
|
摘要
近几年发展起来的基于高阶统计量的混合相位地震子波估计方法,往往假设地层反射系数序列是服从非高斯分布的统计独立的随机过程而不是高斯白噪声分布,因此更加符合实际情况。本文提出的方法首先运用地震道高阶统计量的三谱相位信息重建地震子波的相位谱,并通过地震道的自相关得到子波的振幅谱,从而提取出混合相位的地震子波;然后通过改进的柯西约束最大后验反褶积方法实现地震道的反褶积。通过仿真试验和对实际地震资料的处理,表明最大后验反褶积可以在不降低地震资料信噪比的前提下提高地震资料的分辨率。
The mixed-phase seismic wavelet estimation approach based on high-order statistics developed in resent years often assumed that the reflectivity sequence of strata is non-Gaussian and statistically independent random process rather than Gaussian white noise distribution, which is more consistent with practical situation. The approach presented in the paper first used trispectrum phase information of high-order statistics on the seismic traces to reconstruct phase spectrum of seismic wavelet, and yielded the amplitude spectrum of wavelet by autocorrelation of seismic traces, from which the seismic wavelet with mixed phase could be detected, realizing the deconvolution of seismic traces by improved Cauchy-constrained maximum posteriori deconvolution. It is shown by simulation tests and practical seismic data processing that maximum posteriori deconvolution can improve the resolution of seismic data in a precondition without reducing S/N ratio of seismic data.
The mixed-phase seismic wavelet estimation approach based on high-order statistics developed in resent years often assumed that the reflectivity sequence of strata is non-Gaussian and statistically independent random process rather than Gaussian white noise distribution, which is more consistent with practical situation. The approach presented in the paper first used trispectrum phase information of high-order statistics on the seismic traces to reconstruct phase spectrum of seismic wavelet, and yielded the amplitude spectrum of wavelet by autocorrelation of seismic traces, from which the seismic wavelet with mixed phase could be detected, realizing the deconvolution of seismic traces by improved Cauchy-constrained maximum posteriori deconvolution. It is shown by simulation tests and practical seismic data processing that maximum posteriori deconvolution can improve the resolution of seismic data in a precondition without reducing S/N ratio of seismic data.
引文
[1]Matsuoka T,Ulrych T J.Phase esti mation using the bispetrum.Proceedings of the IEEE,1984,72(10):1403～1411
[2]Lazear G D.Mixed phase wavelet esti mation using fourth order cumulants.Geophysics,1993,58(7):1042～1051
[3]Velis D R,Ulrych T J.Si mulated annealing wavelet esti mation via fourth order cumulant matching.Geo-physics,1996,61(6):1939～1948
[4]Wiggins R.Mini mum entropy deconvolution.Geoe-xploration,1978,16:21～35
[5]Kormylo J J,Mendel J M.Maxi mumlikelihood de-tection and esti mation of Bernoulli-Gaussian proces-ses.IEEE Trans inform theory,1982,28(3):482～488
[6]Canadas G.A mathematical framework for blind de-convolution inversion problems.Expanded Abstracts of72nd SEG Mtg,2002
[7]Anthony Larue et al.Blind frequency deconvolution:A newapproach using mutual information rate.Expanded Abstracts of74th SEG Mtg,2004
[8]Ignacio Santamar et al.Deconvolution of seismic data using adaptive gaussian mixtures.IEEE Trans on Geoscience and Remote Sensing,1999,37(3):855～859
[9]Cheng QS et al.Si multaneous wavelet esti mation and deconvolution of reflection seismic signals.IEEE Trans on Geoscience and Rremote Sensing,1996,34(3):377～384
[10]Porsani MJ et al.Mixed-phase deconvolution and wavelet esti mation.The Leading Edge,2000,19(1):76～79
[11]Porsani MJ and Bjorn Ursin.Mixed-phase deconvo-lution.Geophyscis,1998,63(2):637～647
[12]黄绪德.反褶积与地震道反演.北京:石油工业出版社,1992
[13]孟小红等.盲源反褶积方法与应用研究.石油地球物理勘探,2005,40(6):642～645
[14]王锐,孙成俞等.有限熵反褶积.石油地球物理勘探,2005,40(6):637～641
[15]Youzwishen C F.Non-linear sparse and block con-straints for seismic inversion problems.Master's the-sis.University of Albert,2001
[2]Lazear G D.Mixed phase wavelet esti mation using fourth order cumulants.Geophysics,1993,58(7):1042～1051
[3]Velis D R,Ulrych T J.Si mulated annealing wavelet esti mation via fourth order cumulant matching.Geo-physics,1996,61(6):1939～1948
[4]Wiggins R.Mini mum entropy deconvolution.Geoe-xploration,1978,16:21～35
[5]Kormylo J J,Mendel J M.Maxi mumlikelihood de-tection and esti mation of Bernoulli-Gaussian proces-ses.IEEE Trans inform theory,1982,28(3):482～488
[6]Canadas G.A mathematical framework for blind de-convolution inversion problems.Expanded Abstracts of72nd SEG Mtg,2002
[7]Anthony Larue et al.Blind frequency deconvolution:A newapproach using mutual information rate.Expanded Abstracts of74th SEG Mtg,2004
[8]Ignacio Santamar et al.Deconvolution of seismic data using adaptive gaussian mixtures.IEEE Trans on Geoscience and Remote Sensing,1999,37(3):855～859
[9]Cheng QS et al.Si multaneous wavelet esti mation and deconvolution of reflection seismic signals.IEEE Trans on Geoscience and Rremote Sensing,1996,34(3):377～384
[10]Porsani MJ et al.Mixed-phase deconvolution and wavelet esti mation.The Leading Edge,2000,19(1):76～79
[11]Porsani MJ and Bjorn Ursin.Mixed-phase deconvo-lution.Geophyscis,1998,63(2):637～647
[12]黄绪德.反褶积与地震道反演.北京:石油工业出版社,1992
[13]孟小红等.盲源反褶积方法与应用研究.石油地球物理勘探,2005,40(6):642～645
[14]王锐,孙成俞等.有限熵反褶积.石油地球物理勘探,2005,40(6):637～641
[15]Youzwishen C F.Non-linear sparse and block con-straints for seismic inversion problems.Master's the-sis.University of Albert,2001
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心