基于EMD与ICA的地震信号去噪技术研究
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摘要
去除随机噪声是地震资料处理的重要环节,而目前的多数去噪技术都不同程度存在去噪效果差、损害有效信号等问题。为此,利用经验模态分解(Empirical Mode Decomposition,简写为EMD)能将信号自适应分解为不同尺度振动模态的优点及独立分量分析(Independent Component Analysis,简写为ICA)能提取独立源信号的优势,构造了一种EMD与ICA相结合的新的去噪算法,很好地实现了地震有效信号和随机噪声的分离,在提高去噪效果的同时提高了有效信号的保幅效果。将该算法应用于仿真实验和实际资料去噪,结果都明显优于总体经验模态分解(Ensemble Empirical Mode Decomposition,简写为EEMD)去噪结果,地震资料的信噪比和分辨率都大大提高。
Noise attenuation is an important step in seismic data processing.However,most noise attenuation technologies have some problems,such as poor denoising effect and damaging effective noise,more or less.As we all know,Empirical Mode Decomposition(EMD)can self-adaptively decompose signal into multi-scale vibration mode and Independent Component Analysis(ICA)can extract independent source signals.By using these advantages,we constructed a new denoising algorithm by combining EMD with ICA,which can well separate effective signals from random noise and improve the amplitude-preservation effect while improving denoising effect.The algorithm was applied on numerical simulation and actual data for denoising.As a consequent,the results are obviously better than that of Ensemble Empirical Mode Decomposition(EEMD).Meanwhile,the S/N and resolution of seismic data are greatly improved.
Noise attenuation is an important step in seismic data processing.However,most noise attenuation technologies have some problems,such as poor denoising effect and damaging effective noise,more or less.As we all know,Empirical Mode Decomposition(EMD)can self-adaptively decompose signal into multi-scale vibration mode and Independent Component Analysis(ICA)can extract independent source signals.By using these advantages,we constructed a new denoising algorithm by combining EMD with ICA,which can well separate effective signals from random noise and improve the amplitude-preservation effect while improving denoising effect.The algorithm was applied on numerical simulation and actual data for denoising.As a consequent,the results are obviously better than that of Ensemble Empirical Mode Decomposition(EEMD).Meanwhile,the S/N and resolution of seismic data are greatly improved.
引文
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[10]孙云莲,罗卫华,李洪.基于EMD的ICA方法在电力载波通信信号提取中的应用[J].中国电机工程学报,2007,27(16):109-112Sun Y L,Luo W H,Li H.Extract signals of powerline communication by a novel method based onEMD and ICA[J].Proceedings of the CSEE,2007,27(16):109-112
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[12]Wu Z,Huang N E.Ensemble empirical mode decom-position:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41
[13]王婷.EMD算法研究及其在信号去噪中的应用[D].哈尔滨:哈尔滨工程大学,2010Wang T.Research on EMD algorithm and its appli-cation in signal denoising[D].Harbin:Harbin Engi-neering University,2010
[14]晓宇,刘洪.主分量分析和独立分量分析方法的比较研究[J].石油物探,2006,45(5):441-446Xi X Y,L H.Research on comparison of principalcomponent analysis with independent component a-nalysis[J].Geophysical Prospecting for Petroleum,2006,45(5):441-446
[15]Hyvarinen A,Karhunen J,Oja E.Independent Com-ponent Analysis[M].Finland:Wiley-Interscience.2001:182-188
[16]张军华,臧胜涛,周振晓,等.地震资料信噪比定量计算及方法比较[J].石油地球物理勘探,2009,44(4):481-486Zhang J H,Zang S T,Zhou Z X,et al.Quantitativecomputation and comparison of S/N ratio in seismicdata[J].Oil Geophysical Prospecting,2009,44(4):481-486
[2]巩向博,韩立国,王恩利,等.压制噪声的高分辨率Radon变换法[J].吉林大学学报(地球科学版).2009,39(1):152-157Gong X B,Han L G,Wang E L,et al.Denoising viahigh resolution Radon transform[J].Journal of JilinUniversity(Earth Science Edition),2009,39(1):152-157
[3]张孝珍,董汉强,侯国文,等.地震勘探中的去噪技术新进展[J].勘探地球物理进展,2009,32(3):172-178Zhang X Z,Dong H Q,Hou G W,et al.Advances indenoising technology in seismic exploration[J].Pro-gress in Exploration Geophysics,2009,32(3):172-178
[4]史恒,李桂林,王伟,等.基于总体经验模式分解的地震信号随机噪声消除[J].地球物理学进展,2011,26(1):71-78Shi H,Li G L,Wang W,et al.Random noise attenu-ation based on ensemble empirical mode decomposi-tion[J].Progress in Geophysics,2011,26(1):71-78
[5]吴招才,刘天佑.地震数据去噪中的小波方法[J].地球物理学进展,2008,23(2):493-499Wu Z C,Liu T Y.Wavelet transform methods inseismic data noise attenuation[J].Progress in Geo-physics,2008,23(2):493-499
[6]Hyvarinen A,Oja E.Independent component Analy-sis:algorithms and application[J].Neural Net-works,2000,13(4-5):411-430
[7]彭才,朱仕军,孙建库,等.基于独立成分分析的地震数据去噪[J].勘探地球物理进展,2007,30(1):30-32Peng C,Zhu S J,Sun J K,et al.Noise eliminationwith independent component analysis[J].Progress inExploration Geophysics,2007,30(1):30-32
[8]Mijovic B,De Vos M,Gligorijevic I,et al.Sourceseparation from single-channel recordings by combi-ning empirical mode decomposition and independentcomponent analysis[J].IEEE Transaction on Bio-medical Engineering.2010,57(9):2188-2196
[9]Mijovic B,De Vos M,Gligorijevic I,et al.CombiningEMD with ICA for extracting independent sourcesfrom single channel and two-channel data[J].Ex-panded Abstracts of 32nd Annual Internat IEEE EM-BS Mtg,2010,5387-5390
[10]孙云莲,罗卫华,李洪.基于EMD的ICA方法在电力载波通信信号提取中的应用[J].中国电机工程学报,2007,27(16):109-112Sun Y L,Luo W H,Li H.Extract signals of powerline communication by a novel method based onEMD and ICA[J].Proceedings of the CSEE,2007,27(16):109-112
[11]李洪,孙云莲.基于EMD虚拟通道的ICA算法在信号消噪中的应用[J].北京邮电大学学报,2007,30(5):33-36Li H,Sun Y L.Denoising by ICA based on EMDvirtual channel[J].Journal of Beijing University ofPosts and Telecommunications,2007,30(5):33-36
[12]Wu Z,Huang N E.Ensemble empirical mode decom-position:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41
[13]王婷.EMD算法研究及其在信号去噪中的应用[D].哈尔滨:哈尔滨工程大学,2010Wang T.Research on EMD algorithm and its appli-cation in signal denoising[D].Harbin:Harbin Engi-neering University,2010
[14]晓宇,刘洪.主分量分析和独立分量分析方法的比较研究[J].石油物探,2006,45(5):441-446Xi X Y,L H.Research on comparison of principalcomponent analysis with independent component a-nalysis[J].Geophysical Prospecting for Petroleum,2006,45(5):441-446
[15]Hyvarinen A,Karhunen J,Oja E.Independent Com-ponent Analysis[M].Finland:Wiley-Interscience.2001:182-188
[16]张军华,臧胜涛,周振晓,等.地震资料信噪比定量计算及方法比较[J].石油地球物理勘探,2009,44(4):481-486Zhang J H,Zang S T,Zhou Z X,et al.Quantitativecomputation and comparison of S/N ratio in seismicdata[J].Oil Geophysical Prospecting,2009,44(4):481-486
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