Title
3-D Joint Inversion of Gravity and Magnetic Data Using Data-Space and Truncated Gauss-Newton Methods
Abstract
Gravity and magnetic inversion are important methods for comprehensive quantitative interpretation of data obtained in, e.g., mineral, oil and gas, and geothermal exploration. At present, the 3-D joint inversion technology of gravity and magnetic data is facing challenges from large-scale data exploration applications. In this letter, a new algorithm for 3-D joint inversion of gravity and magnetic data with high accuracy and low computational cost is presented. We use the geometric trellis method to perform fast forward calculations and then introduce the sparse constraint and adaptive sensitivity matrix into the model constraint terms. The inexact structural resemblance method is then used to add the cross-gradient constraint penalty term to the objective function. Finally, an algorithm (DS-TGN) combining data-space (DS) and truncated Gauss-Newton (TGN) methods is used to solve the joint inversion objective function. Numerical experiments with synthetic data show that the proposed algorithm can significantly reduce the computational cost and obtain high accuracy density and magnetization models with structural resemblance and sharp boundaries. We also apply the DS-TGN algorithm to data obtained in the area of Greater Khingan in northwestern Heilongjiang, China. The underground density and magnetization distribution results provide a high-resolution geological model for the detection of skarn-type deposits.
Year
DOI
Venue
2022
10.1109/LGRS.2021.3077936
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Keywords
DocType
Volume
Gravity, Mathematical model, Linear programming, Memory management, Magnetic separation, Computational modeling, Jacobian matrices, Cross gradient, data space (DS), gravity method, joint inversion, magnetic method, truncated Gauss-Newton (TGN)
Journal
19
ISSN
Citations 
PageRank 
1545-598X
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Rongzhe Zhang101.35
Tonglin Li202.03
Cai Liu3247.76
Xingguo Huang405.07
Kristian Jensen500.34
Malte Sommer600.34