{"title":"利用共轭梯度进行三维电磁反演","authors":"G. A. Newman, D. Alumbaugh","doi":"10.1109/IGARSS.1997.615302","DOIUrl":null,"url":null,"abstract":"In large scale 3D EM inverse problems it may not be possible to directly invert a full least-squares system matrix involving model sensitivity elements. Thus iterative methods must be employed. For the inverse problem, the authors favor either a linear or nonlinear (NL) CG scheme, depending on the application. In a NL CG scheme, the gradient of the objective function is required at each relaxation step along with a univariate line search needed to determine the optimum model update. Solution examples based on both approaches are presented.","PeriodicalId":64877,"journal":{"name":"遥感信息","volume":"4 1","pages":"933-937 vol.2"},"PeriodicalIF":0.0000,"publicationDate":"1997-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"3D electromagnetic inversion using conjugate gradients\",\"authors\":\"G. A. Newman, D. Alumbaugh\",\"doi\":\"10.1109/IGARSS.1997.615302\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In large scale 3D EM inverse problems it may not be possible to directly invert a full least-squares system matrix involving model sensitivity elements. Thus iterative methods must be employed. For the inverse problem, the authors favor either a linear or nonlinear (NL) CG scheme, depending on the application. In a NL CG scheme, the gradient of the objective function is required at each relaxation step along with a univariate line search needed to determine the optimum model update. Solution examples based on both approaches are presented.\",\"PeriodicalId\":64877,\"journal\":{\"name\":\"遥感信息\",\"volume\":\"4 1\",\"pages\":\"933-937 vol.2\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-08-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"遥感信息\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://doi.org/10.1109/IGARSS.1997.615302\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"遥感信息","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.1109/IGARSS.1997.615302","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
3D electromagnetic inversion using conjugate gradients
In large scale 3D EM inverse problems it may not be possible to directly invert a full least-squares system matrix involving model sensitivity elements. Thus iterative methods must be employed. For the inverse problem, the authors favor either a linear or nonlinear (NL) CG scheme, depending on the application. In a NL CG scheme, the gradient of the objective function is required at each relaxation step along with a univariate line search needed to determine the optimum model update. Solution examples based on both approaches are presented.
期刊介绍:
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