Bayesian subsampling of time-domain electromagnetic data using kernel density product
文献类型:期刊论文
作者 | Li, Hai1,2; Xue, Guoqiang1,2,3; Chen, Wen1,2,3 |
刊名 | GEOPHYSICS
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出版日期 | 2022-03-01 |
卷号 | 87期号:2页码:E79-E90 |
ISSN号 | 0016-8033 |
DOI | 10.1190/geo2021-0258.1 |
英文摘要 | The Bayesian method is a powerful tool to estimate the resistivity distribution and associated uncertainty from time-domain electromagnetic (TDEM) data. Because the forward simulation of the TDEM method is computationally expensive and a large number of samples are needed to globally explore the model space, the full Bayesian inversion of the TDEM data is limited to layered models. To make the high-dimensional Bayesian inversion tractable, we have adopted a divide-and-conquer strategy to speed up the Bayesian inversion of TDEM data. First, the full data sets and model spaces are divided into disjoint batches based on the coverage of the sources so that the independent and highly efficient Bayesian subsampling can be conducted. Then, the samples from each subsampling procedure are combined to get the full posterior. To obtain an asymptotically unbiased approximation to the full posterior, a kernel density product method is used to reintegrate samples from each subposterior. The model parameters and their uncertainty are estimated from the full posterior. Our method is tested on synthetic examples and applied to a field data set acquired with a large fixed-loop configuration. The 2D section from the Bayesian inversion revealed several mineralized zones, one of which matches well with the information from a nearby drillhole. The field example indicates the ability of the Bayesian inversion to infer reliable resistivity and uncertainty. |
WOS关键词 | INVERSION ; TRANSIENT ; FOOTPRINT ; ALGORITHM |
资助项目 | National Science Foundation of China[41804074] ; National Science Foundation of China[42030106] ; IGGCAS Research Start-up Fund[E0515402] ; S&T Program of Beijing[Z181100005718001] |
WOS研究方向 | Geochemistry & Geophysics |
语种 | 英语 |
WOS记录号 | WOS:000776683800006 |
出版者 | SOC EXPLORATION GEOPHYSICISTS |
资助机构 | National Science Foundation of China ; National Science Foundation of China ; National Science Foundation of China ; National Science Foundation of China ; IGGCAS Research Start-up Fund ; IGGCAS Research Start-up Fund ; IGGCAS Research Start-up Fund ; IGGCAS Research Start-up Fund ; S&T Program of Beijing ; S&T Program of Beijing ; S&T Program of Beijing ; S&T Program of Beijing ; National Science Foundation of China ; National Science Foundation of China ; National Science Foundation of China ; National Science Foundation of China ; IGGCAS Research Start-up Fund ; IGGCAS Research Start-up Fund ; IGGCAS Research Start-up Fund ; IGGCAS Research Start-up Fund ; S&T Program of Beijing ; S&T Program of Beijing ; S&T Program of Beijing ; S&T Program of Beijing ; National Science Foundation of China ; National Science Foundation of China ; National Science Foundation of China ; National Science Foundation of China ; IGGCAS Research Start-up Fund ; IGGCAS Research Start-up Fund ; IGGCAS Research Start-up Fund ; IGGCAS Research Start-up Fund ; S&T Program of Beijing ; S&T Program of Beijing ; S&T Program of Beijing ; S&T Program of Beijing ; National Science Foundation of China ; National Science Foundation of China ; National Science Foundation of China ; National Science Foundation of China ; IGGCAS Research Start-up Fund ; IGGCAS Research Start-up Fund ; IGGCAS Research Start-up Fund ; IGGCAS Research Start-up Fund ; 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/132A11/105076] ![]() |
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, Inst Geol & Geophys, Key Lab Mineral Resources, Beijing 100029, Peoples R China 2.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing 100029, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100029, Peoples R China |
推荐引用方式 GB/T 7714 | Li, Hai,Xue, Guoqiang,Chen, Wen. Bayesian subsampling of time-domain electromagnetic data using kernel density product[J]. GEOPHYSICS,2022,87(2):E79-E90. |
APA | Li, Hai,Xue, Guoqiang,&Chen, Wen.(2022).Bayesian subsampling of time-domain electromagnetic data using kernel density product.GEOPHYSICS,87(2),E79-E90. |
MLA | Li, Hai,et al."Bayesian subsampling of time-domain electromagnetic data using kernel density product".GEOPHYSICS 87.2(2022):E79-E90. |
入库方式: OAI收割
来源:地质与地球物理研究所
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