基于稀疏反演的多道匹配追踪地震信号去噪方法及其应用
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摘要
基于超完备子波库的单道匹配追踪方法在自适应分解地震道时未能分离有效信号和噪声,故难以直接、准确地去除噪声。为此提出了基于稀疏反演的多道匹配追踪去噪方法,以多个连续地震道为研究对象,首先采用多方向矢量中值滤波法计算同相轴倾角,以中间道为标准将同相轴校正后再水平叠加;然后采用基于基追踪降噪的稀疏反演方法分解叠加道,选择与叠加道中有效信号匹配的子波作为中间道匹配追踪分解的子波库;最后根据其它道相对中间道的校正量,对中间道匹配追踪分解的子波库进行反校正,分别构建各道匹配追踪分解的子波库。将基于有效信号匹配子波构建的子波库代替超完备子波库,在搜寻最佳匹配子波时选择性地提取地震道中有效信号,可直接分离有效信号和噪声。单道信号测试结果验证了该方法去除噪声的可行性;合成和实际地震数据应用结果表明,该方法相较于单道匹配追踪方法具有更好的去噪效果,且去噪后剖面的横向连续性更好。
Single-channel matching pursuit algorithm based on the over-complete wavelet library does not distinguish effective signals and noise in the process of adaptively decomposing seismic traces.Hence,it is difficult to remove noise directly and accurately.This paper,therefore,proposes a multi-channel matching pursuit denoising method based on sparse inversion.The method takes multiple continuous seismic traces as the research objects.Firstly,multi-directional vector median filtering method is used to calculate the inclination angle of seismic events.These events are corrected to be horizontal using the central trace as the standard,and then stacked.The sparse inversion method based on the basis pursuit denoising is then used to decompose the stack trace.Next,the wavelets matching the effective signal in the stack trace are selected as the wavelet library of the central trace matching pursuit decomposition.Finally,according to the correction amount of other traces relative to the central trace,the wavelet library of the central trace is inversely corrected to construct the wavelet library of each trace matching pursuit decomposition.The wavelet library constructed by matching wavelets of the effective signal replaces the over-complete wavelet library.In the process of searching for the best matching wavelet,the effective signal components in the seismic trace are selectively extracted,so that the effective signal and noise can be directly distinguished.The single-channel signal test result verified the effectiveness of the proposed method to remove noise.The results of the synthetic and real seismic data showed that the proposed method is better at denoising than the single-channel matching pursuit algorithm and can improve the lateral continuity of the seismic sections after denoising.
Single-channel matching pursuit algorithm based on the over-complete wavelet library does not distinguish effective signals and noise in the process of adaptively decomposing seismic traces.Hence,it is difficult to remove noise directly and accurately.This paper,therefore,proposes a multi-channel matching pursuit denoising method based on sparse inversion.The method takes multiple continuous seismic traces as the research objects.Firstly,multi-directional vector median filtering method is used to calculate the inclination angle of seismic events.These events are corrected to be horizontal using the central trace as the standard,and then stacked.The sparse inversion method based on the basis pursuit denoising is then used to decompose the stack trace.Next,the wavelets matching the effective signal in the stack trace are selected as the wavelet library of the central trace matching pursuit decomposition.Finally,according to the correction amount of other traces relative to the central trace,the wavelet library of the central trace is inversely corrected to construct the wavelet library of each trace matching pursuit decomposition.The wavelet library constructed by matching wavelets of the effective signal replaces the over-complete wavelet library.In the process of searching for the best matching wavelet,the effective signal components in the seismic trace are selectively extracted,so that the effective signal and noise can be directly distinguished.The single-channel signal test result verified the effectiveness of the proposed method to remove noise.The results of the synthetic and real seismic data showed that the proposed method is better at denoising than the single-channel matching pursuit algorithm and can improve the lateral continuity of the seismic sections after denoising.
引文
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[11] SIAHSAR M A N,GHOLTASHI S,KAHOO A R,et al.Sparse time-frequency representation for seismic noise reduction using low-rank and sparse decomposition[J].Geophysics,2016,81(2):V117-V124
[12] SHANG S,HAN L G,HU W.Seismic data analysis using synchrosqueezing wavelet transform[J].Expanded Abstracts of 83rd Annual Internat SEG Mtg,2013:4330-4334
[13] DONOHO D L.De-noising by soft-thresholding[J].IEEE Trans Inform Theory,1995,41(3):613-627
[14] MARLLAT S,ZHANG Z.Matching persuits with time-ferquency dictionaries[J].IEEE Transactions on Antennas and Propagation,1993,41(12):3397-3415
[15] 赵天姿,宋炜,王尚旭.基于匹配追踪算法的时频滤波去噪方法[J].石油物探,2008,47(4):367-371ZHAO T Z,SONG W,WANG S X.Time-frequency filtering denoising method based on matching pursuit algorithm[J].Geophysical Prospecting for Petroleum,2008,47(4):367-371
[16] WANG Y H.Multichannel matching pursuit for seismic trace decomposition[J].Geophysics,2010,75(4):V61-V66
[17] FENG X,ZHANG X B,LIU C,et al.Single-channel and multi-channel orthogonal matching pursuit for seismic trace decomposition[J].Journal of Geophysics and Engineering,2017,14(1):90-99
[18] PORTNIAGUINE O,CASTAGNA J.Inverse spectral decomposition[J].Expanded Abstracts of 74th Annual Internat SEG Mtg,2004:1786-1789
[19] HAN L,HAN L G,LI Z.Inverse spectral decomposition with the SPGL1 algorithm[J].Journal of Geophysics and Engineering,2012,9(4):423-427
[20] 张博伦,王林飞,蔡正辉,等.SPGL1算法在地震资料随机噪声压制中的应用[J].中国海洋大学学报,2016,46(12):81-86ZHANG B L,WANG L F,CAI Z H,et al.Application of SPGL1 in random noise suppression of seimic data[J].Periodical of Ocean University of China,2016,46(12):81-86
[21] HUO S,LUO Y,KELAMIS P G.Simultaneous sources separation via multidirectional vector-median filtering[J].Geophysics,2012,77(4):V123-V131
[22] 韩利,刘春成,张益明,等.地震复谱分解技术及其在烃类检测中的应用[J].地球物理学报,2016,59(3):1095-1101HAN L,LIU C C,ZHANG Y M,et al.Seismic complexspectal decomposition and its application on hydrocarbon detection[J].Chinese Journal of Geophysics,2016,59(3):1095-1101
[2] HORNBOSTEL S.Spatial prediction filtering in the t-xand t-xdomains[J].Geophysics,1991,56(12):2019-2026
[3] GACI S.The use of wavelet-based denoising techniques to enhance the first-arrival picking on seismic traces[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(8):4558-4563
[4] HUANG N E,SHEN Z,LONG S R.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the Royal Society of London,Series A:Mathematical,Physical and Engineering Sciences,1998,45(4):903-995
[5] 蔡建华,王先春,胡惟文.基于经验模态分解与小波阈值的MT信号去噪方法[J].石油地球物理勘探,2013,48(2):303-307CAI J H,WANG X C,HU W W.MT signal denoising method based on empirical mode decomposition and wavelet threshold[J].Oil Geophysical Prospecting,2013,48(2):303-307
[6] 李月,彭蛟龙,马海涛,等.过渡内蕴模态函数对经验模态分解去噪结果的影响研究及改进算法[J].地球物理学报,2013,56(2):626-634LI Y,PENG J L,MA H T,et al.Study of the influence of transition IMF on EMD do-noising and the improved algorithm[J].Chinese Journal of Geophysics,2013,56(2):626-634
[7] 秦晅,蔡建超,刘少勇,等.基于经验模态分解互信息熵与同步压缩变换的微地震信号去噪方法研究[J].石油物探,2017,56(5):658-666QIN X,CAI J C,LIU S Y,et al.Microseismic data denoising method based on EMD mutual information entropy and synchrosqueezing transform[J].Geophysical Prospecting for Petroleum,2017,56(5):658-666
[8] 王姣,李振春,王德营.基于CEEMD的地震数据小波阈值去噪方法研究[J].石油物探,2014,53(2):164-172WANG J,LI Z C,WANG D Y.A method for wavelet threshold denoising of seismic data based on CEEMD[J].Geophysical Prospecting for Petroleum,2014,53(2):164-172
[9] DAUBECHIES I,LU J F,WU H T.Synchrosqueezed wavelet transforms:An empirical mode decomposition-like tool[J].Applied and Computational Harmonic Analysis,2011,30(2):243-261
[10] 秦晅,宋维琪.基于同步压缩变换微地震弱信号提取方法研究[J].石油物探,2016,55(1):60-66QIN X,SONG W Q.Weak signal extraction method of microseismic data based on synchrosqueezing transform[J].Geophysical Prospecting for Petroleum,2016,55(1):60-66
[11] SIAHSAR M A N,GHOLTASHI S,KAHOO A R,et al.Sparse time-frequency representation for seismic noise reduction using low-rank and sparse decomposition[J].Geophysics,2016,81(2):V117-V124
[12] SHANG S,HAN L G,HU W.Seismic data analysis using synchrosqueezing wavelet transform[J].Expanded Abstracts of 83rd Annual Internat SEG Mtg,2013:4330-4334
[13] DONOHO D L.De-noising by soft-thresholding[J].IEEE Trans Inform Theory,1995,41(3):613-627
[14] MARLLAT S,ZHANG Z.Matching persuits with time-ferquency dictionaries[J].IEEE Transactions on Antennas and Propagation,1993,41(12):3397-3415
[15] 赵天姿,宋炜,王尚旭.基于匹配追踪算法的时频滤波去噪方法[J].石油物探,2008,47(4):367-371ZHAO T Z,SONG W,WANG S X.Time-frequency filtering denoising method based on matching pursuit algorithm[J].Geophysical Prospecting for Petroleum,2008,47(4):367-371
[16] WANG Y H.Multichannel matching pursuit for seismic trace decomposition[J].Geophysics,2010,75(4):V61-V66
[17] FENG X,ZHANG X B,LIU C,et al.Single-channel and multi-channel orthogonal matching pursuit for seismic trace decomposition[J].Journal of Geophysics and Engineering,2017,14(1):90-99
[18] PORTNIAGUINE O,CASTAGNA J.Inverse spectral decomposition[J].Expanded Abstracts of 74th Annual Internat SEG Mtg,2004:1786-1789
[19] HAN L,HAN L G,LI Z.Inverse spectral decomposition with the SPGL1 algorithm[J].Journal of Geophysics and Engineering,2012,9(4):423-427
[20] 张博伦,王林飞,蔡正辉,等.SPGL1算法在地震资料随机噪声压制中的应用[J].中国海洋大学学报,2016,46(12):81-86ZHANG B L,WANG L F,CAI Z H,et al.Application of SPGL1 in random noise suppression of seimic data[J].Periodical of Ocean University of China,2016,46(12):81-86
[21] HUO S,LUO Y,KELAMIS P G.Simultaneous sources separation via multidirectional vector-median filtering[J].Geophysics,2012,77(4):V123-V131
[22] 韩利,刘春成,张益明,等.地震复谱分解技术及其在烃类检测中的应用[J].地球物理学报,2016,59(3):1095-1101HAN L,LIU C C,ZHANG Y M,et al.Seismic complexspectal decomposition and its application on hydrocarbon detection[J].Chinese Journal of Geophysics,2016,59(3):1095-1101
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