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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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
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Abstract	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.
AbstractList	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.
Author	Yu, Bo Wang, Yufeng Wang, Lingqian Zhou, Hui Fang, Jinwei
Author_xml	– sequence: 1 givenname: Lingqian surname: Wang fullname: Wang, Lingqian organization: State Key Laboratory of Petroleum Resources and Prospecting, Key Laboratory of Geophysical Exploration of CNPC, China University of Petroleum, Beijing, China – sequence: 2 givenname: Hui orcidid: 0000-0002-0166-0073 surname: Zhou fullname: Zhou, Hui email: huizhou@cup.edu.cn organization: State Key Laboratory of Petroleum Resources and Prospecting, Key Laboratory of Geophysical Exploration of CNPC, China University of Petroleum, Beijing, China – sequence: 3 givenname: Yufeng surname: Wang fullname: Wang, Yufeng organization: State Key Laboratory of Petroleum Resources and Prospecting, Key Laboratory of Geophysical Exploration of CNPC, China University of Petroleum, Beijing, China – sequence: 4 givenname: Bo surname: Yu fullname: Yu, Bo organization: State Key Laboratory of Petroleum Resources and Prospecting, Key Laboratory of Geophysical Exploration of CNPC, China University of Petroleum, Beijing, China – sequence: 5 givenname: Jinwei orcidid: 0000-0002-1664-0024 surname: Fang fullname: Fang, Jinwei organization: State Key Laboratory of Petroleum Resources and Prospecting, Key Laboratory of Geophysical Exploration of CNPC, China University of Petroleum, Beijing, China
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SubjectTerms	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
Title	Adaptive Seismic Single-Channel Deconvolution via Convolutional Sparse Coding Model
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