模拟退火独立分量分析方法及其应用
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摘要
独立分量分析通过追求多源混合信号中各个独立源的非高斯性,使各个分量获得统计学上的独立,但现有的诸多独立分量分析方法普遍追求的是运算速度,忽略了对算法本身精确性的要求。为提高分离信号的质量,在原有独立分量分析算法的基础上,结合非线性优化算法——模拟退火算法,提出了一种新的独立分量分析算法——模拟退火独立分量分析算法。给出了独立分量分析方法的基本原理和目标函数；讨论了模拟退火独立分量分析算法的实现过程。数值模拟和实际资料处理表明,该方法在叠前地震记录中能很好地将有效信号和干扰信号分离出来,从本质上起到了压制噪声、突出有效信号的叠前去噪处理作用,改善了叠加剖面的品质。信噪比分析表明,经模拟退火独立分量分析方法去噪处理后,叠加剖面的信噪比由1．3936提高到2．1056。
The independent component analysis is to make each compo- nent be independent statistically through pursuing the nongaussian- ity of each independent source in multiple source composite signals. Most of the existing independent component analysis parsing algo- rithms pursue the computing speed but neglect the accuracy.In or- der to enhance the quality of the separated signal,the simulation annealing independent component parsing algorithm is proposed based on the original independent component parsing algorithm and the simulation annealing algorithm.The basic principles and the objective function of the independent component analysis method have been given and the realizing procedures of the method dis- cussed.The numerical modeling and the actual data processing in- dicate that the method can well separate the effective signals from the interfering signals in prestack seismic record and improve the quality of the stacking profile.By using this de-noise processing method,the signal to noise ratio of the stacking profile can be in- creased from 1.3936 to 2.1056.
The independent component analysis is to make each compo- nent be independent statistically through pursuing the nongaussian- ity of each independent source in multiple source composite signals. Most of the existing independent component analysis parsing algo- rithms pursue the computing speed but neglect the accuracy.In or- der to enhance the quality of the separated signal,the simulation annealing independent component parsing algorithm is proposed based on the original independent component parsing algorithm and the simulation annealing algorithm.The basic principles and the objective function of the independent component analysis method have been given and the realizing procedures of the method dis- cussed.The numerical modeling and the actual data processing in- dicate that the method can well separate the effective signals from the interfering signals in prestack seismic record and improve the quality of the stacking profile.By using this de-noise processing method,the signal to noise ratio of the stacking profile can be in- creased from 1.3936 to 2.1056.
引文
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2 Bell A J,Sejnowski T J.An information maximization approach to blind separation and blind deconvolution[J].Neural Computation,1995,7(6):1 129～1 159
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6 Hyv(?)rinen A,Oja E A.Independent component analysis:Algorithms and applications[J].IEEE Trans Neural Networks,2000,13(4-5):411～430
7刘喜武，刘洪，李幼铭．独立分量分析及其在地震信息处理中的应用初探[J]．地球物理学进展，2003，18(1)：90～96
8刘喜武，刘洪，郑天愉．用独立分量分析方法实现地震转换波与多次反射波分离[J]．防灾减灾工程学报，2003，23(1)：11～19
9郭晓静，吴小培，张道信等．基于独立分量分析的脑电消噪与特征提取[J]．系统仿真学报，2003，15(2)：287～289
2 Bell A J,Sejnowski T J.An information maximization approach to blind separation and blind deconvolution[J].Neural Computation,1995,7(6):1 129～1 159
3 Lee T W.Independent component analysis using an extended infomax algorithm for mixed subgaussian and supgaussian sources[J].Neural Computation,1999,11(2):409～433
4 Lee T W.Blind sources separation of more sources than mixtures using overcomplete representations[J].IEEE Signal Processing Letters,1999,6(4):87～90
5 Hyv(?)rinen A.Fast and robust fixed-point algorithms for independent component analysis[J].IEEE Trans Neural Networks,1999,10(3):626～634
6 Hyv(?)rinen A,Oja E A.Independent component analysis:Algorithms and applications[J].IEEE Trans Neural Networks,2000,13(4-5):411～430
7刘喜武，刘洪，李幼铭．独立分量分析及其在地震信息处理中的应用初探[J]．地球物理学进展，2003，18(1)：90～96
8刘喜武，刘洪，郑天愉．用独立分量分析方法实现地震转换波与多次反射波分离[J]．防灾减灾工程学报，2003，23(1)：11～19
9郭晓静，吴小培，张道信等．基于独立分量分析的脑电消噪与特征提取[J]．系统仿真学报，2003，15(2)：287～289
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