Ying Li, Huifang Zhao, R. He, L. Rao, Xueqin Shen, Weili Yan, D. Khoury, Lijie Feng, Jie Hong, Hongbin Wang, Guizhi Xu
{"title":"基于贝叶斯方法的EIT反问题仿真研究","authors":"Ying Li, Huifang Zhao, R. He, L. Rao, Xueqin Shen, Weili Yan, D. Khoury, Lijie Feng, Jie Hong, Hongbin Wang, Guizhi Xu","doi":"10.1109/CEFC.2010.5481561","DOIUrl":null,"url":null,"abstract":"The prior of blocky resistivity profile is formulated based on the minimal of total variation using Bayesian method, and the resistivity distribution is reconstructed by Maximum a posteriori (MAP). The Markov chain Monte Carlo (MCMC) method with Gibbs sampler is used to sample from posterior density. The simulations on the 2D model show the feasibility of the method.","PeriodicalId":148739,"journal":{"name":"Digests of the 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation","volume":"88 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Simulation study of EIT inverse problem based on Bayesian method\",\"authors\":\"Ying Li, Huifang Zhao, R. He, L. Rao, Xueqin Shen, Weili Yan, D. Khoury, Lijie Feng, Jie Hong, Hongbin Wang, Guizhi Xu\",\"doi\":\"10.1109/CEFC.2010.5481561\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The prior of blocky resistivity profile is formulated based on the minimal of total variation using Bayesian method, and the resistivity distribution is reconstructed by Maximum a posteriori (MAP). The Markov chain Monte Carlo (MCMC) method with Gibbs sampler is used to sample from posterior density. The simulations on the 2D model show the feasibility of the method.\",\"PeriodicalId\":148739,\"journal\":{\"name\":\"Digests of the 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation\",\"volume\":\"88 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Digests of the 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEFC.2010.5481561\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digests of the 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEFC.2010.5481561","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Simulation study of EIT inverse problem based on Bayesian method
The prior of blocky resistivity profile is formulated based on the minimal of total variation using Bayesian method, and the resistivity distribution is reconstructed by Maximum a posteriori (MAP). The Markov chain Monte Carlo (MCMC) method with Gibbs sampler is used to sample from posterior density. The simulations on the 2D model show the feasibility of the method.