Deblending of Simultaneous-Source Seismic Data Based on Deep Convolutional Neural Network-Reference-Cited by-同舟云学术

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Deblending of Simultaneous-Source Seismic Data Based on Deep Convolutional Neural Network

Published:2022 Issue: Volume:60 Page:1-13
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Cheng Jingwang¹^ORCID,Liu Chuncheng²,Zhou Li³,Chen Wei⁴^ORCID,Gu Hanming⁵

Affiliation:

1. College of Mining Engineering, Taiyuan University of Technology, Taiyuan, China

2. CNOOC Research Institute, Beijing, China

3. College of Information and Computer, Taiyuan University of Technology, Taiyuan, China

4. Cooperative Innovation Center of Unconventional Oil and Gas (Ministry of Education & Hubei Province), Yangtze University, Wuhan, China

5. Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, China

Funder

Natural Science of Shanxi Province

China National Major Science and Technology Projects

Open Fund of Cooperative Innovation Center of Unconventional Oil and Gas, Yangtze University

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9633014/09718244.pdf?arnumber=9718244

Reference51 articles.

1. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

2. Deep learning for denoising

3. Hybrid-Sparsity Constrained Dictionary Learning for Iterative Deblending of Extremely Noisy Simultaneous-Source Data

4. Normalized shaping regularization for robust separation of blended data

5. Source separation for simultaneous towed-streamer marine acquisition — A compressed sensing approach

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1. Free-surface multiple attenuation and seismic deghosting for blended data using convolutional neural networks;GEOPHYSICS;2024-04-09

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