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Study on Independent Component Analysis-Based Seismic Blind Deconvolution Method and Its Application

Author: LiuJie
Tutor: YinXingYao
School: China University of Petroleum
Course: Earth Exploration and Information Technology
Keywords: Independent Component Analysis Blind Source Separation Blind deconvolution Seismic signal processing Negative entropy
CLC: P631.4
Type: Master's thesis
Year: 2008
Downloads: 78
Quote: 1
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Abstract


Seismic deconvolution is actually a \Self-start 1978 first blind deconvolution method Wiggins, scholars have proposed a series only from seismic records of their own characteristics starting, do priori assumptions strike seismic wavelet and reflection coefficient method is the so-called \Blind deconvolution problem in the field of seismic signal processing Independent Component Analysis to be long. Independent component analysis (ICA) is associated with blind source separation problem in the 1990s, developed a new signal processing technology. With the gradual improvement of its theoretical system, and achieved success in the field of image processing, biomedical signal and communication systems, finance, speech signal processing, radar and sonar, more and more scholars began to explore how this new technologies, new methods are introduced to the field of geophysics. In this paper, on the basis of previous work, seismic blind deconvolution method based on independent component analysis theory and its applications. Outlines the basic theory and algorithm principle of blind deconvolution, each blind deconvolution method based on categorized according to the research focus at home and abroad that the two types of application wider blind deconvolution method. - based on a Bayesian inversion framework sparse blind deconvolution and based on the theory of independent component analysis blind deconvolution. Then by model and instance analysis shows that the modified Cauchy prior distribution can better protect the weak reflector achieved a better balance between the resolution of seismic data and reduce a weak reflection of repression, and to improve the The resolution and the effectiveness of the reservoir identification. In addition, the combination of independent component analysis theory deconvolution the Bussgang Class algorithm and seismic convolution model feasible based the negative entropy Bussgang blind deconvolution implementations, the theoretical model and the actual data processing show that the method can better adapt non-minimum-phase, non-Gaussian systems are optimal source reflection coefficient estimates, the algorithm converges fast, high precision. Finally, from reality to further explore the independent component analysis in the field of seismic signal processing applications: the actual production of the blind deconvolution-based multi-scale joint Billiton frequency technology; based for low signal-to-noise ratio of seismic data, Independent component analysis of the signal-to-noise separation and blind deconvolution the series processing framework preliminary idea; Independent Component Analysis dimensionality reduction and characteristics in the data extracted advantages blind source seismic attributes optimized hybrid separation process and its treatment process.

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CLC: > Astronomy,Earth Sciences > Geology > Geology, mineral prospecting and exploration > Geophysical exploration > Seismic exploration
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