{"title":"多组态测量电磁干扰数据的平滑稀疏反演","authors":"G. Deidda, Patricia Diaz de Alba, G. Vignoli","doi":"10.1109/RTSI.2018.8548416","DOIUrl":null,"url":null,"abstract":"In this study, we deal with the inversion of frequency-domain electromagnetic data collected with devices with different configurations (varying inter-coil spacing, frequency, height from the ground). More specifically, we present the results of the application of a Gauss-Newton inversion algorithm based on a non-linear forward model onto several synthetic resistivity and magnetic permeability vertical profiles. In addition, we shortly discuss the inclusion into this inversion scheme of a quite novel stabilizing term, based on minimum (gradient) support, and promoting sparse reconstructions. We demonstrate the effectiveness of this sparse inversion algorithm on synthetic and real datasets.","PeriodicalId":363896,"journal":{"name":"2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Smooth and Sparse Inversion of EMI Data from Multi-Configuration Measurements\",\"authors\":\"G. Deidda, Patricia Diaz de Alba, G. Vignoli\",\"doi\":\"10.1109/RTSI.2018.8548416\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, we deal with the inversion of frequency-domain electromagnetic data collected with devices with different configurations (varying inter-coil spacing, frequency, height from the ground). More specifically, we present the results of the application of a Gauss-Newton inversion algorithm based on a non-linear forward model onto several synthetic resistivity and magnetic permeability vertical profiles. In addition, we shortly discuss the inclusion into this inversion scheme of a quite novel stabilizing term, based on minimum (gradient) support, and promoting sparse reconstructions. We demonstrate the effectiveness of this sparse inversion algorithm on synthetic and real datasets.\",\"PeriodicalId\":363896,\"journal\":{\"name\":\"2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RTSI.2018.8548416\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RTSI.2018.8548416","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Smooth and Sparse Inversion of EMI Data from Multi-Configuration Measurements
In this study, we deal with the inversion of frequency-domain electromagnetic data collected with devices with different configurations (varying inter-coil spacing, frequency, height from the ground). More specifically, we present the results of the application of a Gauss-Newton inversion algorithm based on a non-linear forward model onto several synthetic resistivity and magnetic permeability vertical profiles. In addition, we shortly discuss the inclusion into this inversion scheme of a quite novel stabilizing term, based on minimum (gradient) support, and promoting sparse reconstructions. We demonstrate the effectiveness of this sparse inversion algorithm on synthetic and real datasets.