干旱区生态环境知识资源中心(Arid): The Application of Semisupervised Attentional Generative Adversarial Networks in Desert Seismic Data Denoising

Arid

DOI	10.1109/LGRS.2021.3073419
	The Application of Semisupervised Attentional Generative Adversarial Networks in Desert Seismic Data Denoising
	Li, Yue; Luo, Xinming; Wu, Ning; Dong, Xintong
通讯作者	Wu, N (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	2022
英文摘要	For imaging and interpretation, high-quality seismic data are necessary. However, noise, which is strong in field desert seismic data, inevitably diminishes the quality of the data and reduces the signal-to-noise ratio. Moreover, the effective signals and noise in field desert seismic data are mostly distributed in the low-frequency band, which leads to severe spectral aliasing. Recently, some deep learning methods have improved the quality of desert seismic data in certain aspects. However, due to limitations of their networks and the serious spectral aliasing of desert seismic data, the denoising results usually show some false seismic reflections. To solve the above problems, we introduce Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (U-GAT-IT) to the denoising of desert seismic data in a semisupervised manner. U-GAT-IT is an unsupervised attentional generative adversarial network (GAN) combined with an attention module guided by the class activation map (CAM). The attention module guided by the CAM can guide the model to better distinguish between noise and effective signals. The experiment shows that the U-GAT-IT can effectively suppress desert seismic noise. Also, the denoising result has fewer false seismic reflections.
英文关键词	Noise reduction Noise measurement Training Generators Signal to noise ratio Generative adversarial networks Mathematical model Class activation map (CAM) deep learning (DL) desert seismic data generative adversarial networks (GANs)
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000732214700001
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/2XILL650/374578
作者单位	[Li, Yue; Luo, Xinming; Wu, Ning; 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,Luo, Xinming,Wu, Ning,et al. The Application of Semisupervised Attentional Generative Adversarial Networks in Desert Seismic Data Denoising[J],2022.
APA	Li, Yue,Luo, Xinming,Wu, Ning,&Dong, Xintong.(2022).The Application of Semisupervised Attentional Generative Adversarial Networks in Desert Seismic Data Denoising.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS.
MLA	Li, Yue,et al."The Application of Semisupervised Attentional Generative Adversarial Networks in Desert Seismic Data Denoising".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022).