IGGCAS OpenIR  > 油气资源研究院重点实验室
Reconstruction of irregular missing seismic data using conditional generative adversarial networks
Wei, Qing1; Li, Xiangyang1,2; Song, Mingpeng3
2021-11-01
Source PublicationGEOPHYSICS
ISSN0016-8033
Volume86Issue:6Pages:V471-V488
AbstractDuring 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.
DOI10.1190/GEO2020-0644.1
Funding OrganizationEdinburgh 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)
WOS KeywordDATA INTERPOLATION ; ATTENUATION ; RESOLUTION ; RECOVERY
Language英语
Funding ProjectEdinburgh Anisotropy Project (EAP) of the British Geological Survey ; EAP ; British Geological Survey (NERC)
Funding OrganizationEdinburgh 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)
WOS Research AreaGeochemistry & Geophysics
WOS SubjectGeochemistry & Geophysics
WOS IDWOS:000744576300002
PublisherSOC EXPLORATION GEOPHYSICISTS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.iggcas.ac.cn/handle/132A11/104897
Collection油气资源研究院重点实验室
Corresponding AuthorLi, Xiangyang
Affiliation1.China Univ Petr, CNPC Key Lab Geophys Prospecting, Beijing 102249, Peoples R China
2.British Geol Survey, Lyell Ctr, Edinburgh EH14 4AP, Midlothian, Scotland
3.Chinese Acad Sci, Inst Geol & Geophys, Beijing 100029, Peoples R China
Recommended Citation
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.
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