3D inversion modeling of joint gravity and magnetic data based on a sinusoidal correlation constraint

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.