Welcome to Journal of University of Chinese Academy of Sciences,Today is

Independent Component Transformation and its Testing Application on Seismic Noise Elimination

  • Liu Xiwu ,
  • Liu Hong ,
  • Li Youming
Expand
  • 1. Institute of Geology & Geophysics, Chinese Academy of Sciences, Beijing 100029, China;

    2. Graduate School of the Chinese Academy of Sciences, Beijing 100039, China

Received date: 2002-06-19

  Online published: 2003-07-10

Abstract

ICA is a novel stat istical method developed recently, which is used to find a representat ion of the Non-Gaussian mult ivariate data. The representation shows that each component of the computed vector is independent stat istically, or as independent as possible. In application, this kind of transformat ion aims to capture the basic structures of the analyzed data, including features abst raction and separat ion of signals. Presents the fundamental theory and fast algorithms, at the same time, implement the Fast ICA and itsupdated version. Compared w ith PCA or K-L t ransformat ion, proposes the concept of Independent component transformat ion ( ICT). On the basis of analyzing the features of seismic signals, does preliminary studies and try to apply ICA on seismic sig nal processing. Research results show the good perspect ive of ICA application to seismic signal processing.

Cite this article

Liu Xiwu , Liu Hong , Li Youming . Independent Component Transformation and its Testing Application on Seismic Noise Elimination[J]. Journal of University of Chinese Academy of Sciences, 2003 , 20(4) : 488 -492 . DOI: 10.7523/j.issn.2095-6134.2003.4.015

References

[1]A Hyvarinen. Survey on independent component analysis. Neural Computing Surveys, 1999,2:94~128

[2]A Hyvarinen, E Oja. Independent component analysis: algorithm and application. Neural Network, 2000, 13:411~430

[3]T W Lee. Independent component analysis: theory and application. Dordrecht(The Netherlands) :Kluwer Academic Publisher, 1998

[4]A Hyvarinen, E Oja. A fast fixed-point algorithm for independent component analysis. Neural Computation, 1997, 9(7): 1483~1492

[5]Yin-Ming Cheng, Lei Xu. Independent component ordering in ICA time series analysis. Neurocomputing, 2001,41:145~152

Outlines

/