Adaptive Seismic Single-Channel Deconvolution via Convolutional Sparse Coding Model

Seismic deconvolution is a typical ill-posed inverse problem. The regularization technique in terms of different prior information is used for a unique and stable solution. Due to the difference between prior information and the actual subsurface situation, it is hard to obtain a solution with satis...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters Vol. 17; no. 8; pp. 1415 - 1419
Main Authors: Wang, Lingqian, Zhou, Hui, Wang, Yufeng, Yu, Bo, Fang, Jinwei
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Coding
Convolutional codes
Convolutional sparse coding (CSC)
Data mining
Deconvolution
Dictionaries
dictionary learning
Encoding
Glossaries
Ill posed problems
Inverse problems
Learning
Linear programming
Machine learning
Objective function
pursuit
Reconstruction
Reflectance
Regularization
Seismic activity
Seismic data
seismic deconvolution
Seismological data
Seismology
ISSN:1545-598X, 1558-0571
Online Access:Get full text
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Summary:Seismic deconvolution is a typical ill-posed inverse problem. The regularization technique in terms of different prior information is used for a unique and stable solution. Due to the difference between prior information and the actual subsurface situation, it is hard to obtain a solution with satisfactory accuracy and resolution. This letter presents the dictionary learning as an efficient adaptive deconvolution method for the reflectivity reconstruction problem. Considering the curse of dimensionality of conventional dictionary learning and the suboptimal solution of the patch-based dictionary learning, we take the convolutional sparse coding (CSC) model as the dictionary learning method. In this method, the prior information can be obtained from the well-log data in the form of sparse CSC dictionary of reflectivity. On the assumption that the deposition of the subsurface layers is stable, the CSC dictionary extracted from the well-log data can also be applied in the whole work area. The CSC-based deconvolution can be seen as the adaptive deconvolution due to the independence of the assumption made about the reflectivity and seismic data. The process of the adaptive CSC-based deconvolution is divided into three parts. First, the CSC dictionary is learned from the well-log data. Then, the objective function is formulated by combining the CSC dictionary and the single-channel seismic record misfit term for the reconstruction of reflectivity. Finally, the objective function is efficiently solved with the coordinate descent approach. We illustrate the performance of our adaptive deconvolution with synthetic and field seismic data.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2019.2945799