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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
Accelerating High-Resolution Seismic Imaging by Using Deep Learning

文献类型：期刊论文

作者Liu, Wei2,3; Cheng, Qian2,4; Liu, Linong2; Wang, Yun3; Zhang, Jianfeng1
刊名APPLIED SCIENCES-BASEL
出版日期2020-04-01
卷号10期号:7页码:16
关键词seismic imaging high-resolution deep learning acceleration
DOI10.3390/app10072502
英文摘要The emerging applications of deep learning in solving geophysical problems have attracted increasing attention. In particular, it is of significance to enhance the computational efficiency of the computationally intensive geophysical algorithms. In this paper, we accelerate deabsorption prestack time migration (QPSTM), which can yield higher-resolution seismic imaging by compensating absorption and correcting dispersion through deep learning. This is implemented by training a neural network with pairs of small-sized patches of the stacked migrated results obtained by conventional PSTM and deabsorption QPSTM and then yielding the high-resolution imaging volume by prediction with the migrated results of conventional PSTM. We use an encoder-decoder network to highlight the features related to high-resolution migrated results in a high-order dimension space. The training data set of small-sized patches not only reduces the required high-resolution migrated result (for instance, only several inline is required) but leads to a fast convergence in training. The proposed deep-learning approach accelerates the high-resolution imaging by more than 100 times. Field data is used to demonstrate the effectiveness of the proposed method.
WOS关键词IMPLEMENTATION ; INTERPOLATION ; NETWORK
资助项目National Oil and Gas Major Project of China[2017ZX05008-007] ; Open Research Found from Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences[KLOR2018-2] ; National Natural Science Foundation of China[41804129]
WOS研究方向Chemistry ; Engineering ; Materials Science ; Physics
语种英语
WOS记录号WOS:000533356200301
出版者MDPI
资助机构National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; Open Research Found from Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences ; Open Research Found from Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences ; Open Research Found from Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences ; Open Research Found from Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences ; 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 Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; Open Research Found from Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences ; Open Research Found from Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences ; Open Research Found from Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences ; Open Research Found from Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences ; 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 Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; Open Research Found from Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences ; Open Research Found from Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences ; Open Research Found from Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences ; Open Research Found from Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences ; 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 Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; Open Research Found from Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences ; Open Research Found from Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences ; Open Research Found from Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences ; Open Research Found from Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences ; 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/132A11/96919]  
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Zhang, Jianfeng
作者单位1.Southern Univ Sci & Technol, Dept Earth & Space Sci, Shenzhen 518055, Peoples R China
2.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing 100029, Peoples R China
3.China Univ Geosci, Sch Geophys & Informat Technol, Beijing 100083, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Wei,Cheng, Qian,Liu, Linong,et al. Accelerating High-Resolution Seismic Imaging by Using Deep Learning[J]. APPLIED SCIENCES-BASEL,2020,10(7):16.
APA Liu, Wei,Cheng, Qian,Liu, Linong,Wang, Yun,&Zhang, Jianfeng.(2020).Accelerating High-Resolution Seismic Imaging by Using Deep Learning.APPLIED SCIENCES-BASEL,10(7),16.
MLA Liu, Wei,et al."Accelerating High-Resolution Seismic Imaging by Using Deep Learning".APPLIED SCIENCES-BASEL 10.7(2020):16.

入库方式： OAI收割

来源：地质与地球物理研究所

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