Seismic data denoising based on modified K-singular value decomposition algorithm

2017 ◽  
Author(s):  
Xin Tian* ◽  
Kai Zhang ◽  
Zhenchun Li
Keyword(s):  
Singular Value Decomposition ◽  
Seismic Data ◽  
Singular Value ◽  
Decomposition Algorithm ◽  
Value Decomposition

scholarly journals Toward efficacy of piecewise polynomial truncated singular value decomposition algorithm in moving force identification

2019 ◽  
Vol 22 (12) ◽  
pp. 2687-2698 ◽  
Author(s):  
Zhen Chen ◽  
Lifeng Qin ◽  
Shunbo Zhao ◽  
Tommy HT Chan ◽  
Andy Nguyen
Keyword(s):  
Singular Value Decomposition ◽  
Singular Values ◽  
Singular Value ◽  
Decomposition Algorithm ◽  
Identification Accuracy ◽  
Piecewise Polynomial ◽  
Truncated Singular Value Decomposition ◽  
Force Identification ◽  
Moving Force ◽  
Value Decomposition

This article introduces and evaluates the piecewise polynomial truncated singular value decomposition algorithm toward an effective use for moving force identification. Suffering from numerical non-uniqueness and noise disturbance, the moving force identification is known to be associated with ill-posedness. An important method for solving this problem is the truncated singular value decomposition algorithm, but the truncated small singular values removed by truncated singular value decomposition may contain some useful information. The piecewise polynomial truncated singular value decomposition algorithm extracts the useful responses from truncated small singular values and superposes it into the solution of truncated singular value decomposition, which can be useful in moving force identification. In this article, a comprehensive numerical simulation is set up to evaluate piecewise polynomial truncated singular value decomposition, and compare this technique against truncated singular value decomposition and singular value decomposition. Numerically simulated data are processed to validate the novel method, which show that regularization matrix [Formula: see text] and truncating point [Formula: see text] are the two most important governing factors affecting identification accuracy and ill-posedness immunity of piecewise polynomial truncated singular value decomposition.

A General Method for Remontaging Based on a Singular Value Decomposition Algorithm

2003 ◽  
Vol 20 (3) ◽  
pp. 179-187 ◽  
Author(s):  
Terrence D. Lagerlund ◽  
Frank W. Sharbrough ◽  
Neil E. Busacker
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Decomposition Algorithm ◽  
Value Decomposition ◽  
General Method

Tube Wave Suppression in High Frequency Mine Seismic Data by Singular Value Decomposition

1995 ◽  
Vol 26 (2-3) ◽  
pp. 512-517 ◽  
Author(s):  
Geraldine Teakle ◽  
Shunhua Coa ◽  
Stewart Greenhalgh
Keyword(s):  
Singular Value Decomposition ◽  
Seismic Data ◽  
High Frequency ◽  
Singular Value ◽  
Tube Wave ◽  
Value Decomposition
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