基于改进PSO-ICA的地震信号去噪方法
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摘要
为了改善常规固定步长独立分量分析(ICA)算法的叠前地震信号去噪效果,本文提出一种基于混沌粒子群优化(PSO)的改进ICA算法。该算法利用混沌PSO动态调整相对梯度ICA的步长函数,减小ICA算法的稳态误差。在混沌PSO优化过程中,采用一种基于反正切函数的非线性递减惯性权重,提高PSO迭代初期的全局搜索能力和迭代后期的局部搜索能力。模型试算和实际单炮记录处理结果表明:本文提出的改进ICA算法去噪效果明显,有效信号损失小。与其他算法相比,改进ICA算法不仅能很好地保护有效地震信号,而且能提高信噪比。
A modified independent component analysis(ICA) approach based on the chaos particle swarm optimization(PSO) is proposed in this paper in order to improve prestack seismic denoising on the basis of the fixed step size ICA.This method utilizes the chaos PSO algorithm to adjust the step size function of relative gradient ICA,and it can reduce the residual error of ICA.On the other hand,a nonli-nearly decreasing inertia weight(NDIW) is proposed to improve the capability of global and local search of PSO.The trial results for synthetic and shot gather data show that the proposed ICA method works pretty well on removing random noise with very little seismic signal loses compared to other approaches.In addition,the proposed method can obtain high signal-to-noise ratio(SNR) data.
A modified independent component analysis(ICA) approach based on the chaos particle swarm optimization(PSO) is proposed in this paper in order to improve prestack seismic denoising on the basis of the fixed step size ICA.This method utilizes the chaos PSO algorithm to adjust the step size function of relative gradient ICA,and it can reduce the residual error of ICA.On the other hand,a nonli-nearly decreasing inertia weight(NDIW) is proposed to improve the capability of global and local search of PSO.The trial results for synthetic and shot gather data show that the proposed ICA method works pretty well on removing random noise with very little seismic signal loses compared to other approaches.In addition,the proposed method can obtain high signal-to-noise ratio(SNR) data.
引文
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[2]沈鸿雁,李庆春.频域奇异值分解(SVD)地震波场去噪.石油地球物理勘探,2010,45(2):185～189Shen Hong-yan,Li Qing-chun.SVD(singular valuedecomposition)seismic wave field noise elimination infrequency domain.OGP,2010,45(2):185～189
[3]Jutten C,Herault J.Blind separation of sources,partI:an adaptive algorithm based on neuromimetic archi-tecture.Signal Processing,1991,24(1):1～10
[4]Do C M,Javidi B.3Dintegral imaging reconstructionof occluded objects using independent component a-nalysis-based K-means clustering.Journal of Dis-play Technology,2010,6(7):257～262
[5]王明达,张来斌,梁伟等.基于独立分量分析和支持向量机的管道泄漏识别方法.石油学报,2010,31(4):659～663Wang Mingda,Zhang Laibin,Liang Wei et al.Pipelineleakage detection method based on independent compo-nent analysis and support vector machine.Acta PetroleiSinica,2010,31(4):659～663
[6]Walden A T.Non-Gaussian reflectivity,entropy anddeconvolution.Geophysics,1985,50(12):2862～2888
[7]陆文凯,骆毅,赵波等.基于独立分量分析的多次波自适应相减技术.地球物理学报,2004,47(5):886～891Lu Wen-kai,Luo Yi,Zhao Bo et al.Adaptive multi-ple wave subtraction using independent component a-nalysis.Chinese Journal of Geophysics,2004,47(5):886～891
[8]吕文彪,尹成,张白林等.利用独立分量分析法去除地震噪声.石油地球物理勘探,2007,42(2):132～136Lv Wen-biao,Yin Cheng,Zhang Bai-lin et al.Usingindependent component analysis to eliminate seismicnoises.OGP,2007,42(2):132～136
[9]陆斌,葛洪魁,吴何珍等.独立成分分析在随钻地震信号处理中的应用.石油地球物理勘探,2010,45(1):15～22Lu Bin,Ge Hong-kui,Wu He-zhen et al.Applica-tion of independent component analysis in seismicwhile drilling signal processing.OGP,2010,45(1):15～22
[10]Kaplan Sam T,Innanen Kristopher A.Adaptive sep-aration of free-surface multiples through independentcomponent analysis.Geophysics,2008,73(3):V29～V36
[11]李大卫,尹成,谢兵.模拟退火独立分量分析方法及其应用.石油物探,2007,46(1):24～27Li Dawei,Yin Cheng,Xie Bing.Simulated annealingindependent component analysis and its application.GPP,2007,46(1):24～27
[12]朱孝龙,张贤达.基于奇异值分解的超定盲信号分离.电子与信息学报,2004,26(3):337～343Zhu Xiaolong,Zhang Xianda.Overdetermined blindsource separation based on singular value decomposi-tion.Journal of Electronics&Information Tech-nology,2004,26(3):337～343
[13]Kennedy J,Eberhart R C.Particle swarm optimiza-tion.Proceedings of IEEE International Conferenceon Neural Networks.Piscataway(USA):IEEEPress,1995,1942～1948
[14]Shi Y,Eberhart R C.A modified particle swarm op-timizer.Proceedings of IEEE International Confer-ence on Evolutionary Computation.Washington:IEEE Press,1998,69～73
[15]高尚,杨静宇.混沌粒子群优化算法研究.模式识别与人工智能,2006,19(2):266～270Gao Shang,Yang Jingyu.Research on chaos particleswarm optimization algorithm.Pattern Recognitionand Artificial Intelligence,2006,19(2):266～270
[16]Hasler M,Maistrenko Y L.An introduction to thesynchronization of chaotic systems:Coupled skewtent maps.IEEE Transactions on Circuits and Sys-tems I:Fundamental Theory and Applications,1997,44(10):856～866
[17]Zhang Xianda,Zhu Xiaolong and Bao Zheng.Gradinglearning for blind source separation.Science in Chi-na:Series F,2003,46(1):31～44
[18]Chen Sheng,Hong Xia,Harris C J.Particle swarmoptimization aided orthogonal forward regression forunified data modeling.IEEE Transactions on Evolu-tionary Computation,2010,14(4):477～499
[19]Shi Y,Eberhart R C.Empirical study of particleswarm optimization.Proceedings of the 1999 Con-gress on Evolutionary Computation.Washington:IEEE,1999,3:1945～1950
[20]Hsieh Shengta,Sun Tsungying,Lin Chunling et al.Effective learning rate adjustment of blind source sep-aration based on an improved particle swarm optimi-zer.IEEE Transactions on Evolutionary Computa-tion,2008,12(2):242～251
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