De-aliased seismic data interpolation using conditional Wasserstein generative adversarial networks
文献类型：期刊论文
| 作者 | Wei, Qing1; Li, Xiangyang1,3; Song, Mingpeng2 |
| 刊名 | COMPUTERS & GEOSCIENCES
 |
| 出版日期 | 2021-09-01 |
| 卷号 | 154页码:13 |
| 关键词 | De-aliased seismic data interpolation Conditional generative adversarial network Wasserstein distance |
| ISSN号 | 0098-3004 |
| DOI | 10.1016/j.cageo.2021.104801 |
| 英文摘要 | When 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. |
| WOS关键词 | DATA RECONSTRUCTION ; ATTENUATION ; PROJECTION |
| 资助项目 | Edinburgh Anisotropy Project (EAP) of the British Geological Survey |
| WOS研究方向 | Computer Science ; Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:000756945900007 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | 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 ; Edinburgh Anisotropy Project (EAP) of the British Geological Survey |
| 源URL | [http://ir.iggcas.ac.cn/handle/132A11/104905]  |
| 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 | Wei, Qing; Li, Xiangyang |
| 作者单位 | 1.China Univ Petr, CNPC Key Lab Geophys Prospecting, Beijing 102249, Peoples R China 2.Chinese Acad Sci, Inst Geol & Geophys, Beijing 100029, Peoples R China 3.British Geol Survey, Lyell Ctr, Edinburgh EH14 4AP, Midlothian, Scotland |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：地质与地球物理研究所
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