基于固定点算法的地震数据降噪
|
摘要
结合改进的固定点算法,解决了噪声环境下的ICA问题。根据噪声分布特性,分两个阶段去除不同类型的随机噪声。在预处理阶段去除了加性高斯白噪声,预处理后的数据采用改进的固定点算法,盲分离出有效信号和非高斯随机噪声。提出了对固定点算法迭代过程中设定较精确的初始值问题的算法,该方法能较为准确地设置初始值,使算法能提取有效信号。通过仿真实验和对实际地震数据的处理,得到了满意的分离结果,较好地恢复了有效信号。此外,当实际地震数据加载了较强噪声,信噪比降低时,采用本文算法进行盲分离,同样取得了良好的效果,再次验证了本文算法具有良好的稳健性和适应性。将盲分离算法应用到实际地震数据处理方面的研究,有助于地震资料的解释,同时这种处理技术的研究也能够促进盲分离技术的发展。
An improved fixed-point algorithm is used to solve the ICA problem accompanied with noise.According to the noise distribution,it takes two phases to eliminate random noises of different types in preprocessing.The additive white Gaussian noise is removed at first,then the Improved FastICA algorithm is used to process the preprocessed data and to blindly separate the effective signal from non-Gaussian random noise.It might be a problem to set a good starting value in the iterative process of Improved FastICA.In this way,one can accurately set the starting value to make the algorithm recover the effective signal.The satisfactory separation results and better recovery of the effective signal are achieved as shown by the simulation experiments and real seismic data processing.Furthermore,in the case of the strong seismic noise with actually loaded and reduced SNR,this algorithm of blind separation also produces good results.This verifies that the algorithm has good robustness and adaptability.Using the algorithm of blind separation to do the seismic data enhancement can help to better interprete the seismic data,and promote the development of blind separation technology.
An improved fixed-point algorithm is used to solve the ICA problem accompanied with noise.According to the noise distribution,it takes two phases to eliminate random noises of different types in preprocessing.The additive white Gaussian noise is removed at first,then the Improved FastICA algorithm is used to process the preprocessed data and to blindly separate the effective signal from non-Gaussian random noise.It might be a problem to set a good starting value in the iterative process of Improved FastICA.In this way,one can accurately set the starting value to make the algorithm recover the effective signal.The satisfactory separation results and better recovery of the effective signal are achieved as shown by the simulation experiments and real seismic data processing.Furthermore,in the case of the strong seismic noise with actually loaded and reduced SNR,this algorithm of blind separation also produces good results.This verifies that the algorithm has good robustness and adaptability.Using the algorithm of blind separation to do the seismic data enhancement can help to better interprete the seismic data,and promote the development of blind separation technology.
引文
[1]Tong L,Liu R,Soon V C,et al.Indeterminacy and identifiabililty of blind identification[J].IEEE Trans on Circuits and Systems,1991,38(5):499-506.
[2]Cardoso J F.High-order contrasts for independent component analysis[J].Neural Computation,1999,11(1):157-192.
[3]Bell M S,Jung A,Sejnowski T.Independent component analysis of electroencephalographic data[J].Advances in Neural Information Processing Systems,1996,8(6):145-151.
[4]杨福生,洪波.独立分量分析的原理与应用[M].北京:清华大学出版社,2006.Yang Fusheng,Hong Bo.Independent component analysis principle and application[M].Beijing:Tsinghua University Press,2006.
[5]Amaris.Natural gradient works efficiently in learning[J].Neural Computation,1998,10(2):241-276.
[6]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):90-96.Liu Xiwu,Liu Hong,Li Youming.Progress in Geophysics,2003,18(1):90-96.
[7]张发启.盲信号处理及应用[M].西安:西安电子科技大学出版社,2006.Zhang Faqi.Operation and application of blind signal[M].Xi'an:Xi'an University of Electronic Science and Technology Press,2006.
[8]恵晓宇,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):441-446.Hui Xiaoyu,Liu Hong.Geophysical prospecting for petroleum,2006,45(5):441-446.
[9]马建仓,牛奕龙,陈海洋.盲信号处理[M].北京:国防工业出版社,2006.Ma Jiancang,Niu Yilong,Chen Haiyang.Blind signal processing[M].Beijing:National Defense Industry Press,2006.
[10]Lu W,Mao F.Adaptive multiple subtraction using independent component analysis[J].The Leading Edge,2005,24(3):282-284.
[2]Cardoso J F.High-order contrasts for independent component analysis[J].Neural Computation,1999,11(1):157-192.
[3]Bell M S,Jung A,Sejnowski T.Independent component analysis of electroencephalographic data[J].Advances in Neural Information Processing Systems,1996,8(6):145-151.
[4]杨福生,洪波.独立分量分析的原理与应用[M].北京:清华大学出版社,2006.Yang Fusheng,Hong Bo.Independent component analysis principle and application[M].Beijing:Tsinghua University Press,2006.
[5]Amaris.Natural gradient works efficiently in learning[J].Neural Computation,1998,10(2):241-276.
[6]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):90-96.Liu Xiwu,Liu Hong,Li Youming.Progress in Geophysics,2003,18(1):90-96.
[7]张发启.盲信号处理及应用[M].西安:西安电子科技大学出版社,2006.Zhang Faqi.Operation and application of blind signal[M].Xi'an:Xi'an University of Electronic Science and Technology Press,2006.
[8]恵晓宇,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):441-446.Hui Xiaoyu,Liu Hong.Geophysical prospecting for petroleum,2006,45(5):441-446.
[9]马建仓,牛奕龙,陈海洋.盲信号处理[M].北京:国防工业出版社,2006.Ma Jiancang,Niu Yilong,Chen Haiyang.Blind signal processing[M].Beijing:National Defense Industry Press,2006.
[10]Lu W,Mao F.Adaptive multiple subtraction using independent component analysis[J].The Leading Edge,2005,24(3):282-284.
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心