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基于光滑约束模型的大地电磁三维正则化反演(英文)
引用本文:童孝忠,柳建新,郭荣文,刘海飞,龚露.基于光滑约束模型的大地电磁三维正则化反演(英文)[J].中国有色金属学会会刊,2014,24(2):509-513.
作者姓名:童孝忠  柳建新  郭荣文  刘海飞  龚露
作者单位:[1]中南大学地球科学与信息物理学院,长沙410083 [2]中南大学有色资源与地质灾害探查湖南省重点实验室,长沙410083
基金项目:Project (20110162120064) supported by Higher School Doctor Subject Special Scientific Research Foundation of China,Project (10JJ6059) supported by the Natural Science Foundation of Hunan Province, China
摘    要:如何得到快速稳定的反演结果和更清晰地重建地下电阻率结构等问题仍然是当前大地电磁反演研究的一个重点。在构制目标函数中采用正则化方法,使不适定反演问题具有稳定的反演结果,并改善解的稳定性和非唯一性;将光滑约束模型和近似灵敏度矩阵求解方法应用于反演过程中,快速稳定地构建地下电阻率结构。理论模型试算的结果表明光滑约束正则化反演方法是可行的,并能合理地重建地下三维电阻率结构。

关 键 词:大地电磁  正则化反演  近似灵敏度矩阵  光滑约束模型
收稿时间:30 December 2012

Three-dimensional magnetotelluric regularized inversion based on smoothness-constrained model
Xiao-zhong TONG,Jian-xin LIU,Rong-wen GUO,Hai-fei LIU,Lu GONG.Three-dimensional magnetotelluric regularized inversion based on smoothness-constrained model[J].Transactions of Nonferrous Metals Society of China,2014,24(2):509-513.
Authors:Xiao-zhong TONG  Jian-xin LIU  Rong-wen GUO  Hai-fei LIU  Lu GONG
Affiliation:1. School of Geosciences and Info-physics, Central South University, Changsha 410083, China; 2. Key Laboratory of Nonferrous Resource and Geological Hazard Detection of Hunan Province, Central South University, Changsha 410083, China)
Abstract:How to get the rapid and stable inversion results and reconstruct the clear subsurface resistivity structures is a focus problem in current magnetotelluric inversion. A stable solution of an ill-posed inverse problem was obtained by the regularization methods in which some desired structures were imposed to stabilize the inverse problem. By the smoothness-constrained model and approximate sensitivity method, the stable subsurface resistivity structures were reconstructed. The synthetic examples show that the smoothness-constrained regularized inversion method is effective and can be reasonable to reconstruct three-dimensional subsurface resistivity structures.
Keywords:magnetotelluric  regularized inversion  approximate sensitivity  smoothness-constrained model
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