基于多用户峰度准则的海洋强噪声衰减方法(英文)
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摘要
海洋地震勘探过程中,由于采集设备的老化或电源的不稳定而造成的漏电,在地震记录表现为强噪音干扰,利用常规噪音衰减方法处理此类强噪音效果并不理想。鉴于强噪音在统计学上具有相同的特性,本文在基于峰度的盲分离(blind source separation,BSS)算法研究基础上,推导出一种基于多用户峰度(multiuser kurtosis,MUK)准则的噪音衰减算法来估计地震记录中具有相同统计特性的强噪音,并将其从地震记录中分离,从而达到衰减强噪音的目的。模型试验与实际资料的处理表明:该方法能够在好的衰减海洋地震勘探记录中的强噪音,保留了更多的有效信息,提高海洋地震数据的信噪比,具有可行性和应用前景。
The strong noise produced by the leakage of electricity from marine seismic streamers is often received with seismic signals during marine seismic exploration. Traditional denoising methods show unsatisfactory effects when eliminating strong noise of this kind. Assuming that the strong noise signals have the same statistical properties, a blind source separation (BSS) algorithm is proposed in this paper that results in a new denoising algorithm based on the constrained multi-user kurtosis (MUK) optimization criterion. This method can separate strong noise that shares the same statistical properties as the seismic data records and then eliminate them. Theoretical and field data processing all show that the denoising algorithm, based on multi-user kurtosis optimization criterion, is valid for eliminating the strong noise which is produced by the leakage of electricity from the marine seismic streamer so as to preserve more effective signals and increase the signal-noise ratio. This method is feasible and widely applicable.
The strong noise produced by the leakage of electricity from marine seismic streamers is often received with seismic signals during marine seismic exploration. Traditional denoising methods show unsatisfactory effects when eliminating strong noise of this kind. Assuming that the strong noise signals have the same statistical properties, a blind source separation (BSS) algorithm is proposed in this paper that results in a new denoising algorithm based on the constrained multi-user kurtosis (MUK) optimization criterion. This method can separate strong noise that shares the same statistical properties as the seismic data records and then eliminate them. Theoretical and field data processing all show that the denoising algorithm, based on multi-user kurtosis optimization criterion, is valid for eliminating the strong noise which is produced by the leakage of electricity from the marine seismic streamer so as to preserve more effective signals and increase the signal-noise ratio. This method is feasible and widely applicable.
引文
Amari,S.I.,Varinen,H.A.,and Lee,S.Y.,2002,Blindsignal separation and independent component analysis:Neurocomputing,49,1– 5.
Canales,L.L,1984,Random noise reduction: 54thAnnualInternet.Mtg.,Soc.Expl.Geophys.,Expanded Abstracts,Session: S10.1,525– 572.
Cardoso,J.F.,1989,Source separation using higher ordermoments: in Proc IEEE Int Conf Acoust,Speech,SignalProcess,4,2109– 2112.
Comon,P.,1996,Contrasts for multichannel blinddeconvolution: IEEE Signal Processing Lett,3,209– 211.
Comon,P.,1994,Independent components analysis: A newconcept: Signal Process,36(3),287– 314.
Donoho,D.,1981,On minimum entropy deconvolution inapplied time series analysis II: New York,Academic,565– 609.
Franco,R.D.,and Musacchio,G.,2001,Polarization filterwith singular value decomposition: Geophysics,66(3),932– 938.
Galagher,N.C.,and Wise,G.L.,1981,A theoretical analysisof the properties of Median Filters: IEEE Trans on Acoust,Speech and Signal Processing,29(6),1136– 114.
Inouye,Y.,1998,Criteria for blind deconvolution ofmultichannel linear time invariant systems: IEEE TransSignal Processing,Special Issue on Signal Processing forAdvanced Communications,45,268– 271.
Juttern,C.,and Hearault,J.,1991,Blind separation of source,Part I: An adaptive algorithm based on neuromimeticarchitecture: Signal Processing,24,1– 10.
Juttern,C.,and Hearault,J.,1991,Blind separation of source,Part II: An adaptive algorithm based on neuromimeticarchitecture: Signal Processing,24,11– 20.
Kamal,M.,and AL-YAHYA,1993,Application of partialkarhunerr loeve transform to suppress random noise inseismic sections: Geophysical Prospecting,39(1),77– 93.
Li,H.Y.,Hao,R.F.,Ma,J.F.,and Wang,H.K.,2007,Separation of noisy mixed image based on wienerfiltering and independent component analysis: ApplicationResearch of Computers,24(10),161– 162,165.
Mallat,S.,1989,Theory for multi-resolution signaldecomposition: the wavelet representation: IEEETransactions on Pattern Analysis and MachineIntelligence,11(7),674– 693.
Papadias,C.B.,2000,Globally convergent blind sourceseparation based on a multiuser kurtosis maximizationcriterion: IEEE Trans Signal Processing,48(12),3508–3519.
Papadias,C.B.,and Paulraj,A.,1997,Blind separation ofindependent co-channel signals: 13th Int Conf DigitalSignal Process,Santorini Greece,139– 142.
Papadias,C.B.,and Paulraj,A.,1997,A constant modulusalgorithm for multiuser signal separation in presenceof delay spread using antenna arrays: IEEE SignalProcessing Lett,4,178– 181.
Shalvi,O.,and Weinsten,E.,1990,New criteria for blinddeconvolution of nonminimum phase systems (channels):IEEE Trans Inform Theory,36,312– 321.
Shynk,J.J.,and Gooch,R.P.,1996,The constant modulusarray for cochannel signal copy and direction finding:IEEE Trans Signal Processing,44,652– 660.
Swami,A.A.,Giannakis,G.,and Shamsunder,S.,1994,Multichannel ARMA processes: IEEE Trans,Signal Processing,42,898– 913.
Touzni,A.,and Fijalkow,I.,1997,Blind adaptiveequalization and simultaneous separation of convolutivemixtures: Digital Signal Process,391– 394.
Treitel,S.,1974,The complex Wiener filter: Geophysics,39,169– 173.
Tugnait,J.K.,1997,Blind spatiotemporal equalization andimpulse response estimation for MIMO channel usinga Godard cost function: IEEE Trans Signal Processing,Special Issue on Signal Processing for AdvancedCommunications,45,268–271.
Tugnait,J.K.,1997,Identification and deconvolution ofmultichannel linear non-Gaussian processes using higherorder statistics and inverse filter criteria: IEEE TransSignal Processing,45,658– 672.
Van,D.V.A.,and Paulraj,A.,1996,An analytical constantmodulus algorithm: IEEE Trans Signal Processing,44,1136– 1155.
Wei,L.,2004,Blind sources separation based on non-negative matrix factorization: Electronics Optics &Control,11(2),38– 41,53.
Zhang,J.P.,and Liu,A.L.,2008,Applications of blindsource separation based on independent factor analysis:Journal of East China University of Science andTechnology (Natural Science Edition),34(3),410– 416.
Zhu,X.L.,and Zhang,X.D.,2004,Overdetermined blindsource separation based on singular value decomposition:Journal of Electronics and Information Technology,26(3),337– 343.
Zong,T.,Meng,H.Y.,and Jia,Y.L.,Liu,G.Z.,1998,Denoising method based on wavelet packet and forward-backward prediction in F-X domain: Chinese Journal ofGeophysics,41,337– 346.
Canales,L.L,1984,Random noise reduction: 54thAnnualInternet.Mtg.,Soc.Expl.Geophys.,Expanded Abstracts,Session: S10.1,525– 572.
Cardoso,J.F.,1989,Source separation using higher ordermoments: in Proc IEEE Int Conf Acoust,Speech,SignalProcess,4,2109– 2112.
Comon,P.,1996,Contrasts for multichannel blinddeconvolution: IEEE Signal Processing Lett,3,209– 211.
Comon,P.,1994,Independent components analysis: A newconcept: Signal Process,36(3),287– 314.
Donoho,D.,1981,On minimum entropy deconvolution inapplied time series analysis II: New York,Academic,565– 609.
Franco,R.D.,and Musacchio,G.,2001,Polarization filterwith singular value decomposition: Geophysics,66(3),932– 938.
Galagher,N.C.,and Wise,G.L.,1981,A theoretical analysisof the properties of Median Filters: IEEE Trans on Acoust,Speech and Signal Processing,29(6),1136– 114.
Inouye,Y.,1998,Criteria for blind deconvolution ofmultichannel linear time invariant systems: IEEE TransSignal Processing,Special Issue on Signal Processing forAdvanced Communications,45,268– 271.
Juttern,C.,and Hearault,J.,1991,Blind separation of source,Part I: An adaptive algorithm based on neuromimeticarchitecture: Signal Processing,24,1– 10.
Juttern,C.,and Hearault,J.,1991,Blind separation of source,Part II: An adaptive algorithm based on neuromimeticarchitecture: Signal Processing,24,11– 20.
Kamal,M.,and AL-YAHYA,1993,Application of partialkarhunerr loeve transform to suppress random noise inseismic sections: Geophysical Prospecting,39(1),77– 93.
Li,H.Y.,Hao,R.F.,Ma,J.F.,and Wang,H.K.,2007,Separation of noisy mixed image based on wienerfiltering and independent component analysis: ApplicationResearch of Computers,24(10),161– 162,165.
Mallat,S.,1989,Theory for multi-resolution signaldecomposition: the wavelet representation: IEEETransactions on Pattern Analysis and MachineIntelligence,11(7),674– 693.
Papadias,C.B.,2000,Globally convergent blind sourceseparation based on a multiuser kurtosis maximizationcriterion: IEEE Trans Signal Processing,48(12),3508–3519.
Papadias,C.B.,and Paulraj,A.,1997,Blind separation ofindependent co-channel signals: 13th Int Conf DigitalSignal Process,Santorini Greece,139– 142.
Papadias,C.B.,and Paulraj,A.,1997,A constant modulusalgorithm for multiuser signal separation in presenceof delay spread using antenna arrays: IEEE SignalProcessing Lett,4,178– 181.
Shalvi,O.,and Weinsten,E.,1990,New criteria for blinddeconvolution of nonminimum phase systems (channels):IEEE Trans Inform Theory,36,312– 321.
Shynk,J.J.,and Gooch,R.P.,1996,The constant modulusarray for cochannel signal copy and direction finding:IEEE Trans Signal Processing,44,652– 660.
Swami,A.A.,Giannakis,G.,and Shamsunder,S.,1994,Multichannel ARMA processes: IEEE Trans,Signal Processing,42,898– 913.
Touzni,A.,and Fijalkow,I.,1997,Blind adaptiveequalization and simultaneous separation of convolutivemixtures: Digital Signal Process,391– 394.
Treitel,S.,1974,The complex Wiener filter: Geophysics,39,169– 173.
Tugnait,J.K.,1997,Blind spatiotemporal equalization andimpulse response estimation for MIMO channel usinga Godard cost function: IEEE Trans Signal Processing,Special Issue on Signal Processing for AdvancedCommunications,45,268–271.
Tugnait,J.K.,1997,Identification and deconvolution ofmultichannel linear non-Gaussian processes using higherorder statistics and inverse filter criteria: IEEE TransSignal Processing,45,658– 672.
Van,D.V.A.,and Paulraj,A.,1996,An analytical constantmodulus algorithm: IEEE Trans Signal Processing,44,1136– 1155.
Wei,L.,2004,Blind sources separation based on non-negative matrix factorization: Electronics Optics &Control,11(2),38– 41,53.
Zhang,J.P.,and Liu,A.L.,2008,Applications of blindsource separation based on independent factor analysis:Journal of East China University of Science andTechnology (Natural Science Edition),34(3),410– 416.
Zhu,X.L.,and Zhang,X.D.,2004,Overdetermined blindsource separation based on singular value decomposition:Journal of Electronics and Information Technology,26(3),337– 343.
Zong,T.,Meng,H.Y.,and Jia,Y.L.,Liu,G.Z.,1998,Denoising method based on wavelet packet and forward-backward prediction in F-X domain: Chinese Journal ofGeophysics,41,337– 346.
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