基于广义S变换的独立频谱分量盲提取

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英文篇名：Blind Extraction Technology of Independent Spectrum Component Based on Generalized s Transform 中文刊名：石油天然气学报 英文刊名：Journal of Oil and Gas Technology 作者：胥德平 ; 文晓涛 ; 文武 ; 詹泽东 英文作者：XU De-ping ; WEN Xiao-tao ; WEN Wu ; ZHAN Ze-dong Key Laboratory of Geomathematics of Sichuan Province ; College of Management Science ; College of Geophysics ; Chengdu University of Technology ; Chengdu 610059 ; Sichuan ; China) 中文关键词：广义S变换 ; 独立分量分析 ; 时频分析 ; 频谱分解 英文关键词：generalized S transform ; independent component analysis ; time-frequency analysis ; spectral decomposition 出版日期：2011-06-15 机构：数学地质四川省重点实验室成都理工大学管理科学学院;成都理工大学地球物理学院;成都理工大学管理科学学院; 年：2011 期：06 出版单位：石油天然气学报

摘要

广义S变换具有良好的时频聚集性,可以得到用来研究地层信息特征的单频数据体。但单频体分解过多会对地震数据分析带来不便,此外,如果信号在时频域上相距太近,甚至混叠,对地震信号的研究会带来很大困难。基于盲信号处理算法在广义S域中从众多的单频数据体中提取出一些有效的独立频谱分量,来识别地震剖面中的有用特征信息。在仿真试验的基础上针对TH区三叠系进行试算,与井进行的相关分析证实了该方法的有效性。
Generalized S transform had good time-frequency focusing property,and it could be used to obtain the single-frequency data volume for studying the stratum information characteristics.But the overmuch single-frequency data volume would cause inconvenience to seismic data analysis.In additional,if signals were too close or even overlay,it would create difficulty for signal research.The algorithm based on the blind signal processing can extract some effective independent frequency spectrum components distinguishing with the useful characteristic information of seismic profile from the numerous single-frequency data volume.On the basis of simulation test,the method is applied in Triassic in TH Area.It is contrasted with other related analyses,it is confirmed that the method is validate.

引文

[1]Stockwell R G,Mansinha L,Lowe R P.Localization of the complex spectrum:the S transform[J].IEEE Trans on Signal Process-ing,1996,44(4):998～1001.
    [2]高静怀,陈文超,李幼铭,等.广义S变换与薄互层地震响应[J].地球物理学报,2003,46(4):526～532.
    [3]陈学华,贺振华,黄德济.基于广义S变换的信号提取与抑噪[J].成都理工大学学报,2006,33(4):331～335.
    [4]Pinnegar C R,Eaton D W.Application of S transform to prestack noise attenuation filtering[J].Geophys Res,2003,108(B9):1～10.
    [5]Comon P.Independent component analysis,a new concept[J].Signal Processing,1994,36(3):287～314.
    [6]Hyvarinen A,Oja E.A fast fixed-point algorithm for independent component analysis[J].Neural Computation,1997,9(7):1483～1492.
    [7]Bingham E,Hyvarinen A.A fast fixed-point algorithm for independent component analysis of omplex-valued signals[J].Int J Neu-ral Systems,2000,10(1):1～8.
    [8]John P C,Sheng J S,Robert W S.Instantaneous spectral analysis:Detection of low-frequency shadows shadows associated with hy-drocarbons[J].The Leading Edge,2003,22(2):120～127.