基于JADE算法的地震资料随机噪声盲分离方法研究
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摘要
独立分量分析是近年由盲信号理论发展起来的一种新的多维信号处理方法,在没有先验信息的情况下,能够实现源信号的分离。地震资料中常常包含随机噪声,它是由各种不可预知因素综合作用而成,无统一规律。文中将ICA应用于去除随机噪声问题,在对地震数据特点进行分析的基础上,建立了随机噪声盲分离的ICA模型,并对其假设条件和固有不确定问题进行了深入分析,利用文中改进的稳健预处理算法,先去除加性高斯白噪声,然后将预处理后的数据采用JADE盲分离算法分离出有效信号和非高斯分布的随机噪声,并建立能够辨识有效信号的准则,解决了ICA分离后次序不确定问题,实现了有效信号的提取。仿真实验和对实际地震资料处理表明文中提出的算法能够有效地去除随机噪声。
Seismic data usually contains random noise generated from a wide variety of unpredictable irregular factors.Independent Component Analysis(ICA) is a new multi-dimensional signal processing method based on high level statistics able to achieve separation of source signals in the absence of priori information.This article attempts to apply ICA to removing random noise from seismic exploration data and analyzes the assumptions and inherent uncertainties of the technique.We use an improved preprocessing algorithm to remove additive white Gaussian noise(AWGN) first,and then,by joint approximate diagonalization of eigen-matrices(JADE algorithm),further process the data to blindly separate the effective signal from the non-Gaussian distributed random noise.Furthermore,the article establishes the normative similarity coefficient criteria to identify effective signal to resolve the order uncertainty problem in ICA and achieve an effective signal extraction.Simulation experiments and the actual seismic data processing experiments show that the algorithm proposed in this paper effectively removed the random noise.
Seismic data usually contains random noise generated from a wide variety of unpredictable irregular factors.Independent Component Analysis(ICA) is a new multi-dimensional signal processing method based on high level statistics able to achieve separation of source signals in the absence of priori information.This article attempts to apply ICA to removing random noise from seismic exploration data and analyzes the assumptions and inherent uncertainties of the technique.We use an improved preprocessing algorithm to remove additive white Gaussian noise(AWGN) first,and then,by joint approximate diagonalization of eigen-matrices(JADE algorithm),further process the data to blindly separate the effective signal from the non-Gaussian distributed random noise.Furthermore,the article establishes the normative similarity coefficient criteria to identify effective signal to resolve the order uncertainty problem in ICA and achieve an effective signal extraction.Simulation experiments and the actual seismic data processing experiments show that the algorithm proposed in this paper effectively removed the random noise.
引文
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[10]刘喜武,刘洪,李幼铭.独立分量分析及其在地震信息处理中应用初探[J].地球物理学进展,2003,18(1):90-96.
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[13]Yin M C,Xu L.Independent component ordering in ICAti me series anlysis[J].Neurocomputing,2001,41:145-152.
[14]Cichocki A,Amari S.Adaptive Blind Signal and I mage Pro-cessing[M].England:John Wiley&Sons,2004:177-178.
[15]Cardoso J F.High-order for independent component analysis[J].Neural Computation,1999(1):157-192.
[16]Walden A T.Non-Gaussian reflectivity entropy and deconvo-lution[J].Geophysics,1985,50(12):2862-2888.
[2]Gjystdal H,Drottning,Lecomte I,et al.Seismic reser-voir characterization:Including sensitivity to the overburden,survey,and reservoir properties when si mulating the 3Dseis-mic response to the reservoir[J].Earth Science Frontiers,2008,15(1):123-132.
[3]张发启.盲信号处理及应用[M].西安:西安电子科技大学出版社,2006:36-39.
[4]Makeig S,Bell AJ,Sejnowski TJ.Independent componentanalysis of electroencephalographic data[J].Advances inNeural Information Processing Systems,1996,8:145-151.
[5]Bell AJ,Sejnowski TJ.Sejnowski T.Aninformation-maxi-mization approach to blind separation and blind deconvolution[J].Neural Computation,1995,7(6):1129-1159.
[6]Jung T P,Humphries C,Lee T,et al.Extended ICAremovesartifacts fromelectroencephalographic recordings[J].AdvancesinNeural Information Processing Systems,1997,10:894-900.
[7]Bartlett MS,Sejnowski TJ.Independent components of facei mages:Arepresentation for face recognition[C]∥Proceed-ing of the 4th Annual Jount Sysmposiumon Neural Computa-tion,Pasadena,CA,May 17,1997.
[8]惠晓宇,刘洪,曾锐,等.浅水型河流相的ICA地震识别方法[J].地球物理学进展,2006,21(3):856-863.
[9]王绪本,郭勇,王娇,等.基于声波与振动探测的地震灾害生命搜索系统信号分析[J].工程地球物理学报,2005,2(2):79-84.
[10]刘喜武,刘洪,李幼铭.独立分量分析及其在地震信息处理中应用初探[J].地球物理学进展,2003,18(1):90-96.
[11]吕文彪,尹成,张白林,等.利用独立分量分析法去除地震噪声[J].石油地球物理勘探,2007,42(2):132-139.
[12]李景叶,陈小宏,芮振华.基于匹配滤波的多次波压制方法研究[J].地球物理学进展,2007,22(1):200-206.
[13]Yin M C,Xu L.Independent component ordering in ICAti me series anlysis[J].Neurocomputing,2001,41:145-152.
[14]Cichocki A,Amari S.Adaptive Blind Signal and I mage Pro-cessing[M].England:John Wiley&Sons,2004:177-178.
[15]Cardoso J F.High-order for independent component analysis[J].Neural Computation,1999(1):157-192.
[16]Walden A T.Non-Gaussian reflectivity entropy and deconvo-lution[J].Geophysics,1985,50(12):2862-2888.
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