Efficient Convolutional Sparse Coding Based on Penalized Weighted Least Squares for Seismic Data Denoising

Recently, convolutional sparse coding (CSC) has been successfully applied to seismic data denoising. CSC differs from traditional dictionary learning methods based on patching schemes in that it can directly process the whole data and capture correlations between local neighborhoods. However, the le...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing Jg. 62; S. 1 - 12
Hauptverfasser: Bai, Min, Yang, Bo, Wu, Juan, Zhou, Zixiang, Cui, Yang, Ma, Zhaoyang, Zeng, Yang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Coding
Computational efficiency
Computer applications
Convolution
Convolutional codes
Convolutional sparse coding (CSC)
Dictionaries
Encoding
Feature maps
Filters
Least squares
Machine learning
Noise reduction
penalized weighted least square (PWLS)
Random noise
Regularization
Seismic data
seismic data denoising
Seismic response
Singular value decomposition
sparse feature maps
Synthetic data
ISSN:0196-2892, 1558-0644
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Zusammenfassung:Recently, convolutional sparse coding (CSC) has been successfully applied to seismic data denoising. CSC differs from traditional dictionary learning methods based on patching schemes in that it can directly process the whole data and capture correlations between local neighborhoods. However, the learned filters by CSC may contain inaccurate features resulting in the structure loss of data, and solving CSC problems has a heavy computational burden. To optimize these problems, we investigate the denoising accuracy of the CSC model and introduce CSC as a regularization term into the penalized weighted least-squares (PWLSs) framework. In particular, we design an effective method to solve the problem of updating sparse feature maps, which improves computational efficiency. Combining the above two points, we propose an efficient CSC (ECSC) model for random noise attenuation of seismic data. The numerical experiments on synthetic data and field data demonstrate that ECSC performs better than the K-singular value decomposition (K-SVD) algorithm, sequential generalized K-means (SGK) algorithm, and fast and flexible CSC (FF-CSC) in seismic data denoising performance and computational efficiency.
Bibliographie:ObjectType-Article-1
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3459877