Three-dimensional inversion of full magnetic gradient tensor data based on hybrid regularization method

Ji, Shuangxi1,7; Zhang, Huai1,2; Wang, Yanfei3; Rong, Liangliang4; Shi, Yaolin1; Chen, Yongshun5,6

2019-01

10.1111/1365-2478.12721

GEOPHYSICAL PROSPECTING   影响因子和分区

0016-8025

1365-2478

Rapid developments in SQUID-based technology make it possible for geophysical exploration to direct measure, inverse and interpret magnetic gradient tensor data. This contribution introduces a novel three-dimensional hybrid regularization method for inversion of magnetic gradient tensor data, which is based on the minimum support functional and total variation functional. Compared to the existing stabilizers, for example, the minimum support stabilizer, the minimum gradient support stabilizer or the total variation stabilizer, our proposed hybrid stabilizer, in association with boundary penalization, improves the revision result greatly, including higher spatial and depth resolution, more clear boundaries, more highlighted images and more evident structure depiction. Moreover, suitable selection of model parameter lambda will further improve the image quality of the recovered model. We verify our proposed hybrid method with various synthetic magnetic models. Experiment results prove that this method gives more accurate results, exhibiting advantages of less computational costs even when less prior information of magnetic sources are provided. Comparison of results with different types of magnetic data with and without remanence indicates that our inversion algorithm can obtain more detailed information on the source structure based on rational estimation of total magnetization direction. Finally, we present a case study for inverting SQUID-based magnetic tensor data acquired at Da Hinggan Mountains area, inner Mongolia, China. The result also certifies that the method is reliable and efficient for real cases.

Full magnetic gradient tensor data Hybrid parameter regularization Minimum support functional MSTV stabilizer TV regularization

SCI ; EI

英语

其他

Strategic Priority Research Program (B) of the Chinese Academy of Sciences[XDB18010202]

Geochemistry & Geophysics

Geochemistry & Geophysics

WOS:000454407400014

WILEY

20185206311397

Image enhancement ; Molluscs ; Remanence ; Shellfish ; Tensors

Biology:461.9 ; Marine Science and Oceanography:471 ; Magnetism: Basic Concepts and Phenomena:701.2 ; Algebra:921.1

GEOSCIENCES

Web of Science

1.Univ Chinese Acad Sci, Key Lab Computat Geodynam, Beijing 100049, Peoples R China
2.Qingdao Natl Lab Marine Sci & Technol, Lab Marine Mineral Resources, Qingdao 266237, Peoples R China
3.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing 100029, Peoples R China
4.Chinese Acad Sci, Shanghai Inst Microsyst & Informat, CAS Ctr Excellence Superconducting Elect, Shanghai 200050, Peoples R China
5.Peking Univ, Sch Earth & Space Sci, Dept Geophys, Beijing 100871, Peoples R China
6.Southern Univ Sci & Technol, Dept Ocean Sci & Engn, Shenzhen 518055, Peoples R China
7.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

Ji, Shuangxi,Zhang, Huai,Wang, Yanfei,et al. Three-dimensional inversion of full magnetic gradient tensor data based on hybrid regularization method[J]. GEOPHYSICAL PROSPECTING,2019,67(1):226-261.

Ji, Shuangxi,Zhang, Huai,Wang, Yanfei,Rong, Liangliang,Shi, Yaolin,&Chen, Yongshun.(2019).Three-dimensional inversion of full magnetic gradient tensor data based on hybrid regularization method.GEOPHYSICAL PROSPECTING,67(1),226-261.

Ji, Shuangxi,et al."Three-dimensional inversion of full magnetic gradient tensor data based on hybrid regularization method".GEOPHYSICAL PROSPECTING 67.1(2019):226-261.