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Accelerated Bayesian Inversion of Transient Electromagnetic Data Using MCMC Subposteriors

文献类型：期刊论文

作者Li, Hai1,2; Xue, Guoqiang1,2,3; Zhang, Linbo1,2,3
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期2021-12-01
卷号59期号:12页码:10000-10010
关键词Bayes methods Data models Uncertainty Computational modeling Numerical models Proposals Markov processes Bayesian inversion Markov chain Monte Carlo subposterior transient electromagnetic method (TEM)
ISSN号0196-2892
DOI10.1109/TGRS.2020.3035390
英文摘要Transient electromagnetic method (TEM) is one of the major tools to image the subsurface resistivity. The gradient-based inversion of TEM data only provides a unique solution using a subjectively defined regularization penalty, leaving the uncertainty of the solution unaddressed. The Bayesian method can be used to estimate the model parameters, as well as quantify their uncertainty. However, it requires far higher computational costs than gradient-based inversion, which limits the Bayesian inversion of TEM data to 1-D assumptions. We propose an accelerated Bayesian method based on Markov chain Monte Carlo (MCMC) subposteriors to perform full 2-D inversion of TEM data. A robust scheme is designed to divide the model space of a TEM profile into subspaces so that independent MCMC chains can be used to update the parameters in each subspace in parallel. The division is based on the coverage of the source loops using a cumulative sensitivity matrix. Then, the subposteriors obtained at each subspace are merged to approximate the full posterior of the model space using a weighting strategy. A numerical test of a 2-D valley model is used to validate the proposed method. The Bayesian inversion successfully obtained posterior that converges to the true model. The median model makes a good inference of the model parameters, while the probability density function gives their uncertainty estimates. The statistical model of interest can be further extracted from the model ensemble. The proposed method provides an effective framework for Bayesian inversion of TEM data with a multidimensional forward operator.
WOS关键词ALGORITHM
资助项目Natural Science Foundation of China[41804074] ; Natural Science Foundation of China[42030106] ; IGGCAS Research Start-up Founds[E0515402] ; S&T Program of Beijing[Z181100005718001]
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000722170500019
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Natural Science Foundation of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; IGGCAS Research Start-up Founds ; IGGCAS Research Start-up Founds ; IGGCAS Research Start-up Founds ; IGGCAS Research Start-up Founds ; S&T Program of Beijing ; S&T Program of Beijing ; S&T Program of Beijing ; S&T Program of Beijing ; Natural Science Foundation of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; IGGCAS Research Start-up Founds ; IGGCAS Research Start-up Founds ; IGGCAS Research Start-up Founds ; IGGCAS Research Start-up Founds ; S&T Program of Beijing ; S&T Program of Beijing ; S&T Program of Beijing ; S&T Program of Beijing ; Natural Science Foundation of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; IGGCAS Research Start-up Founds ; IGGCAS Research Start-up Founds ; IGGCAS Research Start-up Founds ; IGGCAS Research Start-up Founds ; S&T Program of Beijing ; S&T Program of Beijing ; S&T Program of Beijing ; S&T Program of Beijing ; Natural Science Foundation of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; Natural Science Foundation of China ; IGGCAS Research Start-up Founds ; IGGCAS Research Start-up Founds ; IGGCAS Research Start-up Founds ; IGGCAS Research Start-up Founds ; S&T Program of Beijing ; S&T Program of Beijing ; S&T Program of Beijing ; S&T Program of Beijing
源URL[http://ir.iggcas.ac.cn/handle/132A11/103874]  
专题地质与地球物理研究所_中国科学院矿产资源研究重点实验室
通讯作者Xue, Guoqiang
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 Mineral Resources, Beijing 100029, Peoples R China
3.Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Li, Hai,Xue, Guoqiang,Zhang, Linbo. Accelerated Bayesian Inversion of Transient Electromagnetic Data Using MCMC Subposteriors[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2021,59(12):10000-10010.
APA Li, Hai,Xue, Guoqiang,&Zhang, Linbo.(2021).Accelerated Bayesian Inversion of Transient Electromagnetic Data Using MCMC Subposteriors.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,59(12),10000-10010.
MLA Li, Hai,et al."Accelerated Bayesian Inversion of Transient Electromagnetic Data Using MCMC Subposteriors".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 59.12(2021):10000-10010.

入库方式： OAI收割

来源：地质与地球物理研究所

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