Robust fast dictionary learning for seismic noise attenuation

ABSTRACT Dictionary learning has been intensively applied to process multi‐channel seismic data due to its adaptively learned basis atoms that are data driven. Traditionally, dictionary learning is mostly used to attenuate random noise in the literature since the dictionary update operation is not s...

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Bibliographic Details
Published in:Geophysical Prospecting Vol. 70; no. 7; pp. 1143 - 1162
Main Author: Feng, Zhenjie
Format: Journal Article
Language:English
Published: Houten Wiley Subscription Services, Inc 01.09.2022
Subjects:
Algorithms
Decomposition
Dictionaries
Gaussian distribution
Glossaries
inverse problem
Iterative methods
Learning
Noise reduction
Noise sensitivity
Normal distribution
Random noise
Robustness
Seismic data
signal processing
Singular value decomposition
ISSN:0016-8025, 1365-2478
Online Access:Get full text
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Summary:ABSTRACT Dictionary learning has been intensively applied to process multi‐channel seismic data due to its adaptively learned basis atoms that are data driven. Traditionally, dictionary learning is mostly used to attenuate random noise in the literature since the dictionary update operation is not sensitive to Gaussian noise. However, when dictionary learning is applied to seismic data containing strong erratic noise, which does not follow the Gaussian distribution, its performance greatly deteriorates. In this paper, we propose a novel robust dictionary learning method for dealing with both random and erratic noise. We formulate the dictionary‐learning‐sbased denoising problem as an iterative process. During each iteration, we gradually diminish the effect of the erratic noise and make the denoising problem more Gaussian type. Considering the computational overburden of the classic K‐singular value decomposition algorithm due to many iterations, we substitute the K‐singular value decomposition algorithm with an efficient algorithm, which does not require the singular value decomposition operation. We apply the proposed method to several synthetic and field datasets and obtain good performance, which demonstrates its potential for wide application.
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ISSN:0016-8025
1365-2478
DOI:10.1111/1365-2478.13217