基于动态时间规整ICA算法地震随机噪声压制
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摘要
噪声压制是地震数据处理流程中的基本环节之一。传统的独立分量分析(ICA)算法仅适用于平缓地层同相轴的地震资料噪声压制,对非平缓地层同相轴地震资料去噪效果较差,且算法不够稳定,容易出现解混失败现象,导致去噪结果中产生坏道。针对这些问题,提出了将ICA算法与动态时间规整(DTW)算法相结合的噪声压制方法。首先使用DTW算法将倾斜地层同相轴校正为水平同相轴,利用ICA算法提取拉平后含噪地震数据的独立分量,实现拉平地震道的信噪分离。然后利用由DTW算法所提取的道间时差将同相轴还原为倾斜地层同相轴,从而实现复杂地震资料的随机噪声压制。理论模型和叠前叠后实际地震资料测试结果表明,该方法可以有效地压制地震数据中的随机噪声,且对非平缓地层也有较好的去噪效果,具有一定的实用价值。
Noise suppression is the one of the key issues associated with seismic data processing.The traditional Independent Component Analysis(ICA)algorithm is only suitable for noise suppression in seismic data with flat events.For complex seismic data with non-flat events,the denoising ability of the traditional ICA algorithm is poor,as the algorithm is not stable enough,being prone to demixing failure,resulting in bad sectors in the denoising results.To solve these problems,a noise suppression method combining the ICA algorithm with a dynamic time warping(DTW)algorithm is proposed.Firstly,the DTW is used to flatten tilted seismic data events,and then the ICA algorithm is used to extract the independent component from the noisy data after flattened,and separate the signal from the noise.Next,the moveout between traces extracted by DTW algorithm can be used to restore the flattened events back to the original tilted events,so as to achieve random noise suppression of the complex seismic data.In tests on synthetic data,pre-stack and post-stack data showed that this method can effectively suppress random noise in seismic data,and that it performs well at denoising seismic data with complex non-flat events.
Noise suppression is the one of the key issues associated with seismic data processing.The traditional Independent Component Analysis(ICA)algorithm is only suitable for noise suppression in seismic data with flat events.For complex seismic data with non-flat events,the denoising ability of the traditional ICA algorithm is poor,as the algorithm is not stable enough,being prone to demixing failure,resulting in bad sectors in the denoising results.To solve these problems,a noise suppression method combining the ICA algorithm with a dynamic time warping(DTW)algorithm is proposed.Firstly,the DTW is used to flatten tilted seismic data events,and then the ICA algorithm is used to extract the independent component from the noisy data after flattened,and separate the signal from the noise.Next,the moveout between traces extracted by DTW algorithm can be used to restore the flattened events back to the original tilted events,so as to achieve random noise suppression of the complex seismic data.In tests on synthetic data,pre-stack and post-stack data showed that this method can effectively suppress random noise in seismic data,and that it performs well at denoising seismic data with complex non-flat events.
引文
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[2]印兴耀,刘杰,杨培杰.一种基于负熵的Bussgang地震盲反褶积方法[J].石油地球物理勘探,2007,42(5):499-505YIN X Y,LIU J,YANG P J.A negative entropy-based Bussgang seismic blind deconvolution[J].Oil Geophysical Prospecting,2007,42(5):499-505
[3]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,LIU H.A comparative study of principal component analysis and independent component analysis[J].Geophysical Prospecting for Petroleum,2006,45(5):441-446
[4]李大卫,尹成,谢兵.模拟退火独立分量分析方法及其应用[J].石油物探,2007,46(1):24-27LI D W,YIN C,XIE B.Independent component analysis based on simulated annealing and its application[J].Geophysical Prospecting for Petroleum,2007,46(1):24-27
[5]袁星虎,杨正华,曹剑.Fast ICA在地震信号去噪中的应用研究[J].物探化探计算技术,2017,39(3):378-382YUAN X H,YANG Z H,CAO J.The research and application of Fast ICA in seismic signal denoising[J].Computing Techniques for Geophysical and Geochemical Exploration,2017,39(3):378-382
[6]张银雪,田学民.基于改进PSO-ICA的地震信号去噪方法[J].石油地球物理勘探,2012,47(1):56-62ZHANG Y X,TIAN X M.Seismic denoising based on the modified particle swarm optimization independent component analysis[J].Oil Geophysical Prospecting,2012,47(1):56-62
[7]左博新,胡祥云.基于盲信源分离的地球物理弱异常提取[J].石油地球物理勘探,2014,49(2):375-381ZUO B X,HU X Y.Detection of geophysical weak anomalies based on blind signal separation[J].Oil Geophysical Prospecting,2014,49(2):375-381
[8] COMON P.Independent component analysis,a new concept?[J].Signal Processing,1994,36(3):287-314
[9] CICHOCKI A,DOUGLAS S C,AMARI S.Robust techniques for independent component analysis(ICA)with noisy data[J].Neurocomputing,1998,22(1/2/3):113-129
[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):90-96LIU X W,LIU H,LI Y M.Independent component analysis and its testing application on seismic signal processing[J].Progress in Geophysics,2003,18(1):90-96
[11] LU W K,LUO Y,ZHAO B,et al.Adaptive multiple subtraction using independent component analysis[J].Expanded Abstracts of 73rd Annual Internat SEG Mtg,2003:282-284
[12] MIRKO V D B.PP/PS Wavefield separation by independent component analysis[J].Geophysical Journal International,2006,166(1):339-348
[13] DONNO D.Improving multiple removal using leastsquares dip filters and independent component analysis[J].Geophysics,2011,76(5):V91-V104
[14]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):132-136LV W B,YIN C,ZHANG B L,et al.Using independent component analysis to eliminate seismic noises[J].Oil Geophysical Prospecting,2007,42(2):132-136
[15]吕文彪,尹成,张白林,等.基于独立分量分析的地震属性优化[J].天然气工业,2008,28(9):44-46LV W B,YIN C,ZHANG B L,et al.A combinational optimum method of seismic attributes based on independent component analysis[J].Natural Gas Industry,2008,28(9):44-46
[16]吕文彪,曹中林,张华,等.一种叠前地震资料单频噪声压制新方法[J].石油天然气学报,2014,36(3):65-68LV W B,CAO Z L,ZHANG H,et al.A new method of single frequency noise suppression in prestack seismic data[J].Journal of Oil and Gas Technology,2014,36(3):65-68
[17]王维强,杨国权.基于EMD与ICA的地震信号去噪技术研究[J].石油物探,2012,51(1):19-29WANG W Q,YANG G Q.Research on seismic signal denoising technology based on EMD and ICA[J].Geophysical Prospecting for Petroleum,2012,51(1):19-29
[18]孙成禹,邵婕,蓝阳,等.基于独立分量分析基的地震随机噪声压制[J].石油物探,2016,55(2):196-204SUN C Y,SHAO J,LAN Y,et al.Seismic random noise suppression based on independent component analysis basis functions[J].Geophysical Prospecting for Petroleum,2016,55(2):196-204
[19] CICHOCKI A,AMARI S I.自适应盲信号与图像处理[M].吴正国,唐劲松,章林柯,等译.北京:电子工业出版社,2005:175-178CICHOCKI A,AMARI S I.Adaptive blind signal and image processing[M].WU Z G,TANG J S,ZHANG L K,et al translator.Beijing:Publishing House of Electronics Industry,2005:175-178
[20] BERNDT D J,CLIFFORD J.Using dynamic time warping to find patterns in time series[C]∥AAAIWS'94Proceedings of 3rd International Conference on Knowledge Discovery and Data Mining,1994:359-370
[21] KEOGH E,RATANAMAHATANA C A.Exact indexing of dynamic time warping[J].Knowledge&Information Systems,2005,7(3):358-386
[22] BANKO Z,ABONYI J.Correlation based dynamic time warping of multivariate time series[J].Expert Systems with Applications An International Journal,2012,39(17):12814-12823
[23]孙焘,夏斐,刘洪波.基于动态规划求解时间序列DTW中心[J].计算机科学,2015,42(12):278-282SUN T,XIA F,LIU H B.Calculating DTW center of time series using dynamic planning[J].Computer Science,2015,42(12):278-282
[24]李正欣,张凤鸣,李克武,等.一种支持DTW距离的多元时间序列索引结构[J].软件学报,2014,25(3):560-575LI Z X,ZHANG F M,LI K W,et al.Index structure for multivariate time series under DTW distance metric[J].Journal of Software,2014,25(3):560-575
[25] ZHOU M,WONG M H.Efficient online subsequence searching in data streams under dynamic time warping distance[J].IEEE 24th International Conference on Data Engineering,2008:686-695