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	Seismic data reconstruction based on K-SVD dictionary learning under compressive sensing framework
	Zhou, Yatong ; Wang, Lili ; Pu, Qingshan
	2014
英文摘要	A novel seismic data reconstruction method combined with compressive sensing and K-SVD dictionary learning is presented in this paper to deal with incomplete or irregular seismic data due to acquisition cost or environmental factors. First we obtain an over complete dictionary by training K-SVD dictionary learning on a large number of seismic data samples, then we introduce the sampling matrix of irregular seismic data as a measurement matrix. After that, the seismic data is reconstructed by the regularized orthogonal matching pursuit algorithm. Unlike conventional reconstruction algorithms based on curvelet transform or Fourier transform using single orthogonal basis, the over complete dictionary introduced in this paper extracts adaptively feature through the training samples, and can adaptively select transform basis according to the characteristics of processed data itself. The over complete dictionary provides a flexible way to seismic data sparse extension, and could lead to a better reconstruction result. Experiments on the synthetic seismic data and real marine seismic data have verified the feasibility and efficiency of the method.; EI; 0; 4; 652-660; 49
语种	英语
出处	EI
出版者	shiyou diqiu wuli kantanoil geophysical prospecting
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/328081]
专题	数学科学学院
推荐引用方式 GB/T 7714	Zhou, Yatong,Wang, Lili,Pu, Qingshan. Seismic data reconstruction based on K-SVD dictionary learning under compressive sensing framework. 2014-01-01.

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