De-aliased high-resolution Radon transform based on the sparse prior information from the convolutional neural network
Author:
Affiliation:
1. College of Information Science and Engineering, China University of Petroleum-Beijing , Beijing, 102249 , China
2. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum-Beijing , Beijing, 102249 , China
Abstract
Publisher
Oxford University Press (OUP)
Subject
Management, Monitoring, Policy and Law,Industrial and Manufacturing Engineering,Geology,Geophysics
Link
https://academic.oup.com/jge/article-pdf/19/4/663/44799522/gxac041.pdf
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3. High-resolution 3D tau-p transform by matching pursuit;Cao,2017
4. The simplest discrete Radon transform;Cary,1998
5. Basis pursuit;Chen,1994
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