Seismic image completion method based on information entropy norm

Abstract

The invention discloses a seismic image completion method based on an information entropy norm. The method is applied to the field of geophysical seismic data processing. The problem that a low-rank matrix obtained through recovery based on a matrix rank reduction method is not a real low rank due to the limitation of an existing L1 norm is solved. According to the method, the objective function based on the information entropy norm replaces a nuclear norm adopted by original SVD, then the information entropy norm is used for regularizing a rank minimization item of the objective function, andfinally an iterative ADMM estimation method is used for approximate solution to obtain a low-rank matrix, so that steady recovery of a missing image is obtained.

Description

technical field [0001] The invention belongs to the field of geophysical seismic data processing, and in particular relates to a seismic data complementing technology. Background technique [0002] In the actual geophysical seismic exploration and acquisition, there are two main reasons for the lack of seismic data: (1) The surface environment of the work area (such as rivers, ponds, lakes, etc.), surface obstacles (roads, houses, bridges, etc.) ) and other influences, the layout of the geophone is often irregular; (2) due to the poor coupling effect between the geophone and the ground surface, environmental interference or the instrument itself will produce waste tracks and waste guns. With the development of seismic exploration technology, the volume of collected data is increasing. On the one hand, we hope that densely sampled data can obtain more accurate underground structures. On the other hand, considering the cost of exploration, we need to sparsely sample in the spa...

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Problems solved by technology

[0004] The core of the method based on matrix rank reduction is to use the L1 norm of the singular value vector of the matrix to describe the low rank of seismic data, but the L1 norm description has the problem of insufficient sparse description: for example, for a=[4,4,4 ,0,0] and b=[10,1,1,0,0] two singular value vectors, according to the definition of the L1 norm, it is impossible to distinguish accurately, in fact, the sparsity of the singular value b vector is much higher Due to the singular value a vector, the limitation of the L1 norm leads to the low-rank matrix recovered by the method based on matrix reduction rank is not really low rank

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