干旱区生态环境知识资源中心(Arid): The Denoising of Desert Seismic Data Based on Cycle-GAN With Unpaired Data Training

Arid

DOI	10.1109/LGRS.2020.3011130
	The Denoising of Desert Seismic Data Based on Cycle-GAN With Unpaired Data Training
	Li, Yue; Wang, Hongzhou; Dong, Xintong
通讯作者	Dong, XT (corresponding author), Jilin Univ, Coll Commun Engn, Dept Informat, Changchun 130012, Peoples R China.
来源期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN	1545-598X
EISSN	1558-0571
出版年	2021
卷号	18 期号:11 页码:2016-2020
英文摘要	The seismic data with high quality are the essential foundation of imaging and interpretation. However, the real seismic data are inevitably contaminated by noise, which affects the subsequent processing and interpretation of seismic data. In desert seismic data, the energy of noise is stronger. Also, the frequency-band overlap between noise and effective signals is more serious. Recently, some methods based on supervised learning can suppress the desert seismic noise to some extent. Generally, supervised learning-based methods use synthetic noisy data and paired pure data as training sets to train model. However, the difference between synthetic noisy data of training and real seismic data of testing leads to the degradation of the model, and the denoising results often have many false seismic events when dealing with field seismic data. To solve the above problem, we introduce Cycle-generative adversarial networks (GANs) into the denoising of desert seismic records. Cycle-GAN is an unsupervised learning-based method. It can learn the domain mapping from noisy data domain to effective signal data domain through unpaired data training. So we use unpaired real desert common-shot-point data and synthetic pure data to train Cycle-GAN, so as to effectively improve the denoising ability of the method for real seismic data. Finally, the denoising of desert seismic data is realized. The experiment shows that the Cycle-GAN with unpaired data training can effectively suppress desert seismic noise and retain the effective signal amplitude. Also, the denoising result has less false seismic reflection.
英文关键词	Noise reduction Training Noise measurement Generators Gallium nitride Data models Generative adversarial networks Cycle-generative adversarial networks (GANs) desert seismic data training set unpaired data training
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000711828000035
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368041
作者单位	[Li, Yue; Wang, Hongzhou; Dong, Xintong] Jilin Univ, Coll Commun Engn, Dept Informat, Changchun 130012, Peoples R China
推荐引用方式 GB/T 7714	Li, Yue,Wang, Hongzhou,Dong, Xintong. The Denoising of Desert Seismic Data Based on Cycle-GAN With Unpaired Data Training[J],2021,18(11):2016-2020.
APA	Li, Yue,Wang, Hongzhou,&Dong, Xintong.(2021).The Denoising of Desert Seismic Data Based on Cycle-GAN With Unpaired Data Training.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,18(11),2016-2020.
MLA	Li, Yue,et al."The Denoising of Desert Seismic Data Based on Cycle-GAN With Unpaired Data Training".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 18.11(2021):2016-2020.