{"title":"假设静态偏移为高斯分布的巴布亚新几内亚大地电磁数据集二维反演","authors":"Y. Ogawa","doi":"10.5636/JGG.49.857","DOIUrl":null,"url":null,"abstract":"The Papua New Guinea Magnetotelluric dataset was analyzed by applying Groom-Bailey tensor decomposition, and a consistent strike direction of N66° W was determined. The dataset was approximated by two-dimensional impedances, and frequency independent twist and shear. The static shift parameters (local anisotropy and site gain) were determined using a two-dimensional inversion where static shifts were also part of the model parameters. The model misfit was simultaneously minimized together with the following two norms: (1) roughness norm of the model, and (2) static shift L2 norm. The trade-off parameters between the model misfit and these norms were determined so as to minimize the Akaike's Bayesian Information Criterion (ABIC).","PeriodicalId":156587,"journal":{"name":"Journal of geomagnetism and geoelectricity","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Two-Dimensional Inversion of Papua New Guinea Magnetotelluric Dataset Assuming Static Shift as a Gaussian Distribution\",\"authors\":\"Y. Ogawa\",\"doi\":\"10.5636/JGG.49.857\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Papua New Guinea Magnetotelluric dataset was analyzed by applying Groom-Bailey tensor decomposition, and a consistent strike direction of N66° W was determined. The dataset was approximated by two-dimensional impedances, and frequency independent twist and shear. The static shift parameters (local anisotropy and site gain) were determined using a two-dimensional inversion where static shifts were also part of the model parameters. The model misfit was simultaneously minimized together with the following two norms: (1) roughness norm of the model, and (2) static shift L2 norm. The trade-off parameters between the model misfit and these norms were determined so as to minimize the Akaike's Bayesian Information Criterion (ABIC).\",\"PeriodicalId\":156587,\"journal\":{\"name\":\"Journal of geomagnetism and geoelectricity\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of geomagnetism and geoelectricity\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5636/JGG.49.857\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of geomagnetism and geoelectricity","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5636/JGG.49.857","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
摘要
应用Groom-Bailey张量分解对巴布亚新几内亚大地电磁数据进行分析,确定了N66°W的一致走向方向。数据集由二维阻抗和频率无关的扭转和剪切近似。静态位移参数(局部各向异性和站点增益)使用二维反演确定,其中静态位移也是模型参数的一部分。同时利用两个范数(1)模型粗糙度范数和(2)静态位移L2范数使模型失配最小化。为了使赤池贝叶斯信息准则(Akaike’s Bayesian Information Criterion, ABIC)最小化,确定了模型失拟与这些规范之间的权衡参数。
Two-Dimensional Inversion of Papua New Guinea Magnetotelluric Dataset Assuming Static Shift as a Gaussian Distribution
The Papua New Guinea Magnetotelluric dataset was analyzed by applying Groom-Bailey tensor decomposition, and a consistent strike direction of N66° W was determined. The dataset was approximated by two-dimensional impedances, and frequency independent twist and shear. The static shift parameters (local anisotropy and site gain) were determined using a two-dimensional inversion where static shifts were also part of the model parameters. The model misfit was simultaneously minimized together with the following two norms: (1) roughness norm of the model, and (2) static shift L2 norm. The trade-off parameters between the model misfit and these norms were determined so as to minimize the Akaike's Bayesian Information Criterion (ABIC).