QQ客服 官方微博 反馈留言

	Adaptive multiple subtraction using independent component analysis
	Lu, Wenkai
	2010-05-06 ; 2010-05-06 ; SEP,OCT
关键词	SEPARATION Geochemistry & Geophysics
中文摘要	Adaptive multiple subtraction is a critical and challenging procedure for the widely used surface-related multiple attenuation (SRMA) techniques. In this paper, I present an adaptive multiple subtraction algorithm based on independent component analysis (ICA). The method expresses the problem of adaptive multiple subtraction as a blind source separation (BSS) problem with two mixtures (the seismic data and the predicted multiple) of two or more sources (primaries and multiples). By taking advantage of the sparse property of the seismic data, the method adopts a geometric ICA method to recover the mixing matrix and a linear programming technique to recover the sources when more than two sources are included. The major advantage of the proposed method is that it does not require that the multiples and primaries in the data be orthogonal to each other; that is, the method can perform adaptive multiple subtraction when multiples and primaries have overlap. Furthermore, by expressing the problem of adaptive multiple subtraction as an underdetermined BSS model (more sources with less mixtures), the method can separate the primaries and the multiples when there is time delay and amplitude inconsistency between the true and the predicted multiples. The proposed method is demonstrated on several synthetic datasets generated by simple convolution and finite-difference modeling.
语种	英语 ; 英语
出版者	SOC EXPLORATION GEOPHYSICISTS ; TULSA ; 8801 S YALE ST, TULSA, OK 74137 USA
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/9490]
专题	清华大学
推荐引用方式 GB/T 7714	Lu, Wenkai. Adaptive multiple subtraction using independent component analysis[J],2010, 2010, SEP,.
APA	Lu, Wenkai.(2010).Adaptive multiple subtraction using independent component analysis..
MLA	Lu, Wenkai."Adaptive multiple subtraction using independent component analysis".(2010).

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。