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  2. 地质与地球物理研究所
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Improving the image quality of elastic reverse-time migration in the dip-angle domain using deep learning

文献类型：期刊论文

作者Lu, Yongming4,5; Sun, Hui3; Wang, Xiaoyi3; Liu, Qiancheng2; Zhang, Hao1
刊名GEOPHYSICS
出版日期2020-09-01
卷号85期号:5页码:S269-S283
ISSN号0016-8033
DOI10.1190/GEO2019-0250.1
英文摘要Elastic reverse-time migration (ERTM) is becoming increasingly feasible with the development of high-performance computing. It can provide more physical information on subsurface structures. However, the crosstalk artifacts degrade the imaging resolution of ERTM. To obtain high-resolution ERTM imaging, we have developed additional constraints through a convolutional neural network (CNN) in the dip-angle domain. This procedure can significantly improve the image quality of ERTM by recognizing the dominant reflection events and rejecting the crosstalk artifacts in the dip-angle domain. This method can be divided into the following three steps. First, we generate the dip-angle gathers of ERTM using Poynting vectors shot by shot. Then, we stack all the dip-angle gathers over all the shots. Finally, we adopt the CNN to predict the dip-angle constraint, which can suppress the crosstalk artifacts and enhance the ERTM image quality. The picking method using CNN is an end-to-end procedure that can perform automatic picking without additional human intervention once the network is well-trained. The numerical examples have verified the potential of our method.
WOS关键词NEURAL-NETWORKS ; PRESTACK ; RESOLUTION ; VECTOR ; MEDIA
资助项目National Natural Science Foundation of China[41904051] ; National Natural Science Foundation of China[41804129] ; China Postdoctoral Science Foundation[2018T110137] ; Center for Computational Science and Engineering of the Southern University of Science and Technology
WOS研究方向Geochemistry & Geophysics
语种英语
WOS记录号WOS:000588496500015
出版者SOC EXPLORATION GEOPHYSICISTS
资助机构National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; Center for Computational Science and Engineering of the Southern University of Science and Technology ; Center for Computational Science and Engineering of the Southern University of Science and Technology ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; Center for Computational Science and Engineering of the Southern University of Science and Technology ; Center for Computational Science and Engineering of the Southern University of Science and Technology ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; Center for Computational Science and Engineering of the Southern University of Science and Technology ; Center for Computational Science and Engineering of the Southern University of Science and Technology ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; Center for Computational Science and Engineering of the Southern University of Science and Technology ; Center for Computational Science and Engineering of the Southern University of Science and Technology
源URL[http://ir.iggcas.ac.cn/handle/132A11/99884]  
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Lu, Yongming
作者单位1.Chinese Acad Geol Sci, Inst Geomech, Beijing 100081, Peoples R China
2.Princeton Univ, Dept Geosci, Princeton, NJ 08544 USA
3.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing 100029, Peoples R China
4.Univ Sci & Technol China, Sch Earth & Space Sci, Hefei 230026, Peoples R China
5.Southern Univ Sci & Technol, Dept Earth & Space Sci, Shenzhen 518055, Peoples R China
推荐引用方式
GB/T 7714		 Lu, Yongming,Sun, Hui,Wang, Xiaoyi,et al. Improving the image quality of elastic reverse-time migration in the dip-angle domain using deep learning[J]. GEOPHYSICS,2020,85(5):S269-S283.
APA Lu, Yongming,Sun, Hui,Wang, Xiaoyi,Liu, Qiancheng,&Zhang, Hao.(2020).Improving the image quality of elastic reverse-time migration in the dip-angle domain using deep learning.GEOPHYSICS,85(5),S269-S283.
MLA Lu, Yongming,et al."Improving the image quality of elastic reverse-time migration in the dip-angle domain using deep learning".GEOPHYSICS 85.5(2020):S269-S283.

入库方式： OAI收割

来源：地质与地球物理研究所

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