IGGCAS OpenIR  > 油气资源研究院重点实验室
De-aliased seismic data interpolation using conditional Wasserstein generative adversarial networks
Wei, Qing1; Li, Xiangyang1,2; Song, Mingpeng3
2021-09-01
Source PublicationCOMPUTERS & GEOSCIENCES
ISSN0098-3004
Volume154Pages:13
AbstractWhen sampling at offset is too coarse during seismic acquisition, spatial aliasing will appear, affecting the accuracy of subsequent processing. The receiver spacing can be reduced by interpolating one or more traces between every two traces to remove the spatial aliasing. And the seismic data with spatial aliasing can be seen as regular missing data. Deep learning is an efficient method for seismic data interpolation. We propose to interpolate the regular missing seismic data to remove the spatial aliasing by using conditional generative adversarial networks (cGAN). Wasserstein distance, which can avoid gradient vanishing and mode collapse, is used in training cGAN (cWGAN) to improve the quality of the interpolated data. One velocity model is designed to simulate the training dataset. Test results on different seismic datasets show that the cWGAN with Wasserstein distance is an accurate way for de-aliased seismic data interpolation. Unlike the traditional interpolation methods, cWGAN can avoid the assumptions of low-rank, sparsity, or linearity of seismic data. Besides, once the neural network is trained, we do not need to test different parameters for the best interpolation result, which will improve efficiency.
KeywordDe-aliased seismic data interpolation Conditional generative adversarial network Wasserstein distance
DOI10.1016/j.cageo.2021.104801
Funding OrganizationEdinburgh Anisotropy Project (EAP) of the British Geological Survey ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey
WOS KeywordDATA RECONSTRUCTION ; ATTENUATION ; PROJECTION
Language英语
Funding ProjectEdinburgh Anisotropy Project (EAP) of the British Geological Survey
Funding OrganizationEdinburgh Anisotropy Project (EAP) of the British Geological Survey ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey
WOS Research AreaComputer Science ; Geology
WOS SubjectComputer Science, Interdisciplinary Applications ; Geosciences, Multidisciplinary
WOS IDWOS:000756945900007
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.iggcas.ac.cn/handle/132A11/104905
Collection油气资源研究院重点实验室
Corresponding AuthorWei, Qing; Li, 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. De-aliased seismic data interpolation using conditional Wasserstein generative adversarial networks[J]. COMPUTERS & GEOSCIENCES,2021,154:13.
APA Wei, Qing,Li, Xiangyang,&Song, Mingpeng.(2021).De-aliased seismic data interpolation using conditional Wasserstein generative adversarial networks.COMPUTERS & GEOSCIENCES,154,13.
MLA Wei, Qing,et al."De-aliased seismic data interpolation using conditional Wasserstein generative adversarial networks".COMPUTERS & GEOSCIENCES 154(2021):13.
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