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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
Reconstruction of irregular missing seismic data using conditional generative adversarial networks

文献类型：期刊论文

作者Wei, Qing3; Li, Xiangyang2,3; Song, Mingpeng1
刊名GEOPHYSICS
出版日期2021-11-01
卷号86期号:6页码:V471-V488
ISSN号0016-8033
DOI10.1190/GEO2020-0644.1
英文摘要During acquisition, due to economic and natural reasons, irregular missing seismic data are always observed. To improve accuracy in subsequent processing, the missing data should be interpolated. A conditional generative adversarial network (cGAN) consisting of two networks, a generator and a discriminator, is a deep-learning model that can be used to interpolate the missing data. However, because cGAN is typically data set oriented, the trained network is unable to interpolate a data set from an area different from that of the training data set. We design a cGAN based on Pix2Pix GAN to interpolate irregular missing seismic data. A synthetic data set synthesized from two models is used to train the network. Furthermore, we add a Gaussian-noise layer in the discriminator to fix a vanishing gradient, allowing us to train a more powerful generator. Two synthetic data sets synthesized by two new geologic models and two field data sets are used to test the trained cGAN. The test results and the calculated recovered signal-to-noise ratios indicate that although the cGAN is trained using synthetic data, the network can reconstruct irregular missing field seismic data with high accuracy using the Gaussian-noise layer. We test the performances of cGANs trained with different patch sizes in the discriminator to determine the best structure, and we train the networks using different training data sets for different missing rates, demonstrating the best training data set. Compared with conventional methods, the cGANbased interpolation method does not need different parameter selections for different data sets to obtain the best interpolation data. Furthermore, it is also an efficient technique as the cost is because of the training, and after training, the processing time is negligible.
WOS关键词DATA INTERPOLATION ; ATTENUATION ; RESOLUTION ; RECOVERY
资助项目Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; EAP ; British Geological Survey (NERC)
WOS研究方向Geochemistry & Geophysics
语种英语
WOS记录号WOS:000744576300002
出版者SOC EXPLORATION GEOPHYSICISTS
资助机构Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; EAP ; EAP ; British Geological Survey (NERC) ; British Geological Survey (NERC) ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; EAP ; EAP ; British Geological Survey (NERC) ; British Geological Survey (NERC) ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; EAP ; EAP ; British Geological Survey (NERC) ; British Geological Survey (NERC) ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; EAP ; EAP ; British Geological Survey (NERC) ; British Geological Survey (NERC)
源URL[http://ir.iggcas.ac.cn/handle/132A11/104897]  
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, Xiangyang
作者单位1.Chinese Acad Sci, Inst Geol & Geophys, Beijing 100029, Peoples R China
2.British Geol Survey, Lyell Ctr, Edinburgh EH14 4AP, Midlothian, Scotland
3.China Univ Petr, CNPC Key Lab Geophys Prospecting, Beijing 102249, Peoples R China
推荐引用方式
GB/T 7714		 Wei, Qing,Li, Xiangyang,Song, Mingpeng. Reconstruction of irregular missing seismic data using conditional generative adversarial networks[J]. GEOPHYSICS,2021,86(6):V471-V488.
APA Wei, Qing,Li, Xiangyang,&Song, Mingpeng.(2021).Reconstruction of irregular missing seismic data using conditional generative adversarial networks.GEOPHYSICS,86(6),V471-V488.
MLA Wei, Qing,et al."Reconstruction of irregular missing seismic data using conditional generative adversarial networks".GEOPHYSICS 86.6(2021):V471-V488.

入库方式： OAI收割

来源：地质与地球物理研究所

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