中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地质与地球物理研究所
  3. 中国科学院地质与地球物理研究所
  4. 中国科学院矿产资源研究重点实验室
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
出版日期2022-03-01
卷号87期号:2页码:E79-E90
ISSN号0016-8033
DOI10.1190/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

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。