3D inversion modeling of joint gravity and magnetic data based on a sinusoidal correlation constraint*
Gao Xiu-He, Xiong Sheng-Qing, Zeng Zhao-Fa, Yu Chang-Chun, Zhang Gui-Bin, and Sun Si-Yuan
1. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China.
2. School of Geophysics and Information Technology, China University of Geosciences (Beijing), Beijing 100029, China.
3. College of GeoExploration Science and Technology, Jilin University, Changchun 130000, China.
Abstract Joint inversion based on a correlation constraint utilizes a linear correlation function as a structural constraint. The linear correlation function contains a denominator, which may result in a singularity as the objective function is optimized, leading to an unstable inversion calculation. To improve the robustness of this calculation, this paper proposes a new method in which a sinusoidal correlation function is employed as the structural constraint for joint inversion instead of the conventional linear correlation function. This structural constraint does not contain a denominator, thereby preventing a singularity. Compared with the joint inversion method based on a cross-gradient constraint, the joint inversion method based on a sinusoidal correlation constraint exhibits good performance. An application to actual data demonstrates that this method can process real data.
About author: Gao Xiu-He, Ph.D., graduated from the College of GeoExploration Science and Technology, Jilin University in 2019. She is a postdoctoral researcher at the China Aero Geophysical Survey and Remote Sensing Center for Natural Resources and China University of Geosciences (Beijing). Her primary research interests include 3D inversion and joint inversion of gravity, magnetic, and tensor gradient data. E-mail:gaoxiuhevip@163.com, 842091616@qq.com
Cite this article:
. 3D inversion modeling of joint gravity and magnetic data based on a sinusoidal correlation constraint*[J]. APPLIED GEOPHYSICS, 2019, 16(4): 526-536.
2019, Vol. 16 
