EFGW-UNet: A Deep-Learning-Based Approach for Weak Signal Recovery in Seismic Data
Author:
Affiliation:
1. Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology (Ministry of Education), Jilin, China
2. College of Instrumentation and Electrical Engineering, Jilin University, Changchun, Jilin, China
Funder
National Natural Science Foundation of China
Scientific and Technological Developing Scheme Project of Jilin Province
Natural Science Foundation of Jilin Province
Doctoral Research Foundation of Northeast Electric Power University
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/36/10354519/10522751.pdf?arnumber=10522751
Reference74 articles.
1. Multiscale Residual Pyramid Network for Seismic Background Noise Attenuation
2. RMCHN: A Residual Modular Cascaded Heterogeneous Network for Noise Suppression in DAS-VSP Records
3. RCEN: A Deep-Learning-Based Background Noise Suppression Method for DAS-VSP Records
4. Random and Coherent Noise Suppression in DAS-VSP Data by Using a Supervised Deep Learning Method
5. Seismic data gathering
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