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  4. 中国科学院油气资源研究重点实验室
A Global and Multiscale Denoising Method Based on Generative Adversarial Network for DAS VSP Data

文献类型：期刊论文

作者Ma, Haitao2; Yu, Jingye2; Wang, Yibo1,2; Wu, Ning2; Li, Yue2
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期2023
卷号61页码:15
关键词Distributed acoustic sensing (DAS) generative adversarial network (GAN) global information discriminator multiscale noise suppression U-Net
ISSN号0196-2892
DOI10.1109/TGRS.2023.3318982
英文摘要Distributed acoustic sensing (DAS) has been gradually applied to vertical seismic profiling (VSP), where the generated DAS VSP seismic data contains types of complex noise. Therefore, data denoising plays an important role in collecting high-quality geological information. Generative adversarial network (GAN) has been widely used in seismic exploration data denoising these years, but problems such as insufficient optimization objectives, poor signal retention continuity, and insufficient accuracy still remain when processing DAS VSP data. To address these problems, this article proposes DuGAN, a deep learning network for multiscale feature extraction and global information discrimination, to better meet the requirements of high-precision in DAS VSP data denoising. Our method takes GAN as the basic architecture and chooses the multiscale codec network U-Net to explore the potential correlation of DAS data at different scales and a more robust feature representation of DAS signals. In addition, DuGAN is more inclined to emphasize the global role of the discriminator so that the entire network ensures the integrity of effective signal structure from a global perspective. Also, for more accurate recovery of the DAS reflected signal, we adjust the loss function in adversarial training and tilt the target optimized space toward the discriminator. Experiments on synthetic and field DAS seismic data show that DuGAN has better denoising performance, not only the noise-covered signal can be recovered, but also the overall effective events are better preserved.
WOS关键词NOISE ; DECOMPOSITION ; MIGRATION ; CNN
资助项目National Key Research and Development Project[2021YFA0716800] ; National Natural Science Foundation of China[42230805] ; National Natural Science Foundation of China[42174153]
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001097460500006
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Project ; National Key Research and Development Project ; National Key Research and Development Project ; National Key Research and Development Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Project ; National Key Research and Development Project ; National Key Research and Development Project ; National Key Research and Development Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Project ; National Key Research and Development Project ; National Key Research and Development Project ; National Key Research and Development Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Project ; National Key Research and Development Project ; National Key Research and Development Project ; National Key Research and Development Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China
源URL[http://ir.iggcas.ac.cn/handle/132A11/110669]  
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Wang, Yibo; Wu, Ning
作者单位1.Chinese Acad Sci, Inst Geol & Geophys, Beijing 100029, Peoples R China
2.Jilin Univ, Dept Informat, Coll Commun Engn, Changchun 130012, Peoples R China
推荐引用方式
GB/T 7714		 Ma, Haitao,Yu, Jingye,Wang, Yibo,et al. A Global and Multiscale Denoising Method Based on Generative Adversarial Network for DAS VSP Data[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2023,61:15.
APA Ma, Haitao,Yu, Jingye,Wang, Yibo,Wu, Ning,&Li, Yue.(2023).A Global and Multiscale Denoising Method Based on Generative Adversarial Network for DAS VSP Data.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,61,15.
MLA Ma, Haitao,et al."A Global and Multiscale Denoising Method Based on Generative Adversarial Network for DAS VSP Data".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 61(2023):15.

入库方式： OAI收割

来源：地质与地球物理研究所

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