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  2. 地质与地球物理研究所
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  4. 中国科学院油气资源研究重点实验室
Residual Learning of Cycle-GAN for Seismic Data Denoising

文献类型：期刊论文

作者Li, Wenda1,2,3; Wang, Jian1,2
刊名IEEE ACCESS
出版日期2021
卷号9页码:11585-11597
关键词Noise reduction Training Neural networks Generators Generative adversarial networks Signal to noise ratio Testing Geophysical data geophysics computing generative adversarial networks noise reduction residual learning
ISSN号2169-3536
DOI10.1109/ACCESS.2021.3049479
英文摘要Random noise attenuation has always been an indispensable step in the seismic exploration workflow. The quality of the results directly affects the results of subsequent inversion and migration imaging. This paper proposes a cycle-GAN denoising framework based on the data augmentation strategy. We introduced residual learning into the cycle-GAN to improve the training efficiency of the network. We proposed a method for generating labeled datasets directly from unlabeled real noisy data. Then we significantly improve the diversity of the training samples through an augmentation strategy. Through RCGAN, we can realize intelligent seismic data denoising work, which dramatically reduces the manual selection and intervention of denoising parameters. Finally, numerical experiments prove that our method has a remarkably good random noise suppression ability and a minimally damaging effect on useful seismic signals. The experiment tests on synthetic and real data also show the effectiveness and superiority of the proposed method RCGAN compared to the state-of-the-art denoising methods.
资助项目National Natural Science Foundation of China[U20B200166] ; Major State Research Development Program of China[2016YFC0601101]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000609810200001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Major State Research Development Program of China ; Major State Research Development Program of China ; Major State Research Development Program of China ; Major State Research Development Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Major State Research Development Program of China ; Major State Research Development Program of China ; Major State Research Development Program of China ; Major State Research Development Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Major State Research Development Program of China ; Major State Research Development Program of China ; Major State Research Development Program of China ; Major State Research Development Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Major State Research Development Program of China ; Major State Research Development Program of China ; Major State Research Development Program of China ; Major State Research Development Program of China
源URL[http://ir.iggcas.ac.cn/handle/132A11/100029]  
专题地质与地球物理研究所_中国科学院油气资源研究重点实验室
通讯作者Li, Wenda
作者单位1.Chinese Acad Sci, Inst Earth Sci, Beijing 100029, Peoples R China
2.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing 100029, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Li, Wenda,Wang, Jian. Residual Learning of Cycle-GAN for Seismic Data Denoising[J]. IEEE ACCESS,2021,9:11585-11597.
APA Li, Wenda,&Wang, Jian.(2021).Residual Learning of Cycle-GAN for Seismic Data Denoising.IEEE ACCESS,9,11585-11597.
MLA Li, Wenda,et al."Residual Learning of Cycle-GAN for Seismic Data Denoising".IEEE ACCESS 9(2021):11585-11597.

入库方式： OAI收割

来源：地质与地球物理研究所

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