Similarity-Informed Self-Learning and Its Application on Seismic Image Denoising-Reference-Cited by-同舟云学术

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Similarity-Informed Self-Learning and Its Application on Seismic Image Denoising

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:

Liu Naihao¹^ORCID,Wang Jiale²,Gao Jinghuai¹^ORCID,Chang Shaojie³,Lou Yihuai⁴^ORCID

Affiliation:

1. School of Information and Communications Engineering, Xi’an Jiaotong University, Shaanxi, Xi’an, China

2. School of Software Engineering, Xi’an Jiaotong University, Shaanxi, Xi’an, China

3. Department of Radiology, Mayo Clinic, Rochester, MN, USA

4. Center for Hypergravity Experimental and Interdisciplinary Research, Zhejiang University, Hangzhou, Zhejiang, China

Funder

National Key Research and Development Program 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/ielx7/36/9633014/09904602.pdf?arnumber=9904602

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1. A high-quality self-supervised image denoising method based on SDDW-GAN and CHRNet;Expert Systems with Applications;2024-12

2. Mutual-guided scale-aggregation denoising network for seismic noise attenuation;Computers & Geosciences;2024-09

3. Transfer learning-based hybrid deep learning method for gas-bearing distribution prediction with insufficient training samples and uncertainty analysis;Energy;2024-07

4. Optimizing a transformer-based network for a deep-learning seismic processing workflow;GEOPHYSICS;2024-06-20

5. A hybrid model based on unsupervised learning for seismic response feature extraction and gas-bearing reservoir prediction using longitudinal and transverse waves seismic data in low-degree exploration areas: A case study;Journal of Applied Geophysics;2024-06

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