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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
Reparameterized full-waveform inversion using deep neural networks

文献类型：期刊论文

作者He, Qinglong1,2,3,4; Wang, Yanfei1,2,4
刊名GEOPHYSICS
出版日期2021
卷号86期号:1页码:V1-V13
ISSN号0016-8033
DOI10.1190/GEO2019-0382.1
英文摘要Full-waveform inversion (FWI) is a powerful method for providing a high-resolution description of the subsurface. However, the misfit function of the conventional FWI method (metric l(2)-norm) is usually dominated by spurious local minima owing to its nonlinearity and ill-posedness. In addition, FWI requires intensive wavefield computation to evaluate the gradient and step length. We have considered a general inversion method using a deep neural network (DNN) for the FWI problem. This deep-learning inversion method reparameterizes physical parameters using the weights of a DNN, such that the inversion amounts to reconstructing these weights. One advantage of this deep-learning inversion method is that it can serve as an iterative regularization method, benefiting from the representation of the network. Thus, it is suitable to solve ill-posed nonlinear inverse problems. Furthermore, this method possesses good computational efficiency because it only requires first-order derivatives. In addition, it can easily be accelerated by using multiple graphics processing units and central processing units, for weight updating and forward modeling. Synthetic experiments, based on the Marmousi2, 2004 BP, and a metal ore model, are used to show the numerical performance of the deep-learning inversion method. Comprehensive comparisons with a total-variation regularized FWI are presented to show the ability of our method to recover sharp boundaries. Our numerical results indicate that this deep-learning inversion approach is effective, efficient, and can capture salient features of the model.
WOS关键词PERFECTLY MATCHED LAYER ; FREQUENCY-DOMAIN ; OPTIMAL TRANSPORT ; ALGORITHM ; GRADIENT ; SOLVER ; STRATEGIES ; 2D
资助项目National Natural Science Foundation of China[11801111] ; National Natural Science Foundation of China[91630202] ; National Key R&D Program of the Ministry of Science and Technology of China[2018YFC0603500] ; China Postdoctoral Science Foundation[2019M650831] ; Guizhou Science and Technology Plan Project[[2019]1122] ; Guizhou Science and Technology Platform talents[[2018]5781]
WOS研究方向Geochemistry & Geophysics
语种英语
WOS记录号WOS:000620735200001
出版者SOC EXPLORATION GEOPHYSICISTS
资助机构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 Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; Guizhou Science and Technology Plan Project ; Guizhou Science and Technology Plan Project ; Guizhou Science and Technology Plan Project ; Guizhou Science and Technology Plan Project ; Guizhou Science and Technology Platform talents ; Guizhou Science and Technology Platform talents ; Guizhou Science and Technology Platform talents ; Guizhou Science and Technology Platform talents ; 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 Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; Guizhou Science and Technology Plan Project ; Guizhou Science and Technology Plan Project ; Guizhou Science and Technology Plan Project ; Guizhou Science and Technology Plan Project ; Guizhou Science and Technology Platform talents ; Guizhou Science and Technology Platform talents ; Guizhou Science and Technology Platform talents ; Guizhou Science and Technology Platform talents ; 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 Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; Guizhou Science and Technology Plan Project ; Guizhou Science and Technology Plan Project ; Guizhou Science and Technology Plan Project ; Guizhou Science and Technology Plan Project ; Guizhou Science and Technology Platform talents ; Guizhou Science and Technology Platform talents ; Guizhou Science and Technology Platform talents ; Guizhou Science and Technology Platform talents ; 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 Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; National Key R&D Program of the Ministry of Science and Technology of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; Guizhou Science and Technology Plan Project ; Guizhou Science and Technology Plan Project ; Guizhou Science and Technology Plan Project ; Guizhou Science and Technology Plan Project ; Guizhou Science and Technology Platform talents ; Guizhou Science and Technology Platform talents ; Guizhou Science and Technology Platform talents ; Guizhou Science and Technology Platform talents
源URL[http://ir.iggcas.ac.cn/handle/132A11/101010]  
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Wang, Yanfei
作者单位1.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing 100029, Peoples R China
2.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing 100029, Peoples R China
3.Guizhou Univ, Sch Math & Stat, Guiyang 550025, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 He, Qinglong,Wang, Yanfei. Reparameterized full-waveform inversion using deep neural networks[J]. GEOPHYSICS,2021,86(1):V1-V13.
APA He, Qinglong,&Wang, Yanfei.(2021).Reparameterized full-waveform inversion using deep neural networks.GEOPHYSICS,86(1),V1-V13.
MLA He, Qinglong,et al."Reparameterized full-waveform inversion using deep neural networks".GEOPHYSICS 86.1(2021):V1-V13.

入库方式： OAI收割

来源：地质与地球物理研究所

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