{"title":"基于迭代集合卡尔曼平滑的地震波形反演","authors":"M. Gineste, J. Eidsvik, Y. Zheng","doi":"10.3997/2214-4609.201900038","DOIUrl":null,"url":null,"abstract":"Summary The seismic inverse problem is considered in a Bayesian framework and uses a sequential filtering approach to invert for elastic parameters. The method employs an iterative ensemble smoother to estimate the subsurface parameters and from the ensemble, an estimation uncertainty can be extracted. The sequential filtering conditions over partitions of the entire data record in order to drive the estimation process in a top-down manner and regularize the inversion process. The method is presented with a synthetic example using seismic shot record for a 1D medium.","PeriodicalId":350524,"journal":{"name":"Second EAGE/PESGB Workshop on Velocities","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Seismic Waveform Inversion Using an Iterative Ensemble Kalman Smoother\",\"authors\":\"M. Gineste, J. Eidsvik, Y. Zheng\",\"doi\":\"10.3997/2214-4609.201900038\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary The seismic inverse problem is considered in a Bayesian framework and uses a sequential filtering approach to invert for elastic parameters. The method employs an iterative ensemble smoother to estimate the subsurface parameters and from the ensemble, an estimation uncertainty can be extracted. The sequential filtering conditions over partitions of the entire data record in order to drive the estimation process in a top-down manner and regularize the inversion process. The method is presented with a synthetic example using seismic shot record for a 1D medium.\",\"PeriodicalId\":350524,\"journal\":{\"name\":\"Second EAGE/PESGB Workshop on Velocities\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Second EAGE/PESGB Workshop on Velocities\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3997/2214-4609.201900038\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Second EAGE/PESGB Workshop on Velocities","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3997/2214-4609.201900038","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Seismic Waveform Inversion Using an Iterative Ensemble Kalman Smoother
Summary The seismic inverse problem is considered in a Bayesian framework and uses a sequential filtering approach to invert for elastic parameters. The method employs an iterative ensemble smoother to estimate the subsurface parameters and from the ensemble, an estimation uncertainty can be extracted. The sequential filtering conditions over partitions of the entire data record in order to drive the estimation process in a top-down manner and regularize the inversion process. The method is presented with a synthetic example using seismic shot record for a 1D medium.