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.

中文翻译：

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基于正弦相关约束的联合重力和磁数据的3D反演建模

基于相关约束的联合反演利用线性相关函数作为结构约束。线性相关函数包含一个分母，当优化目标函数时可能导致奇异性，从而导致反演计算不稳定。为了提高这种计算的鲁棒性，本文提出了一种新方法，其中使用正弦相关函数代替联合线性相关函数作为联合反演的结构约束。该结构约束不包含分母，从而防止了奇异性。与基于交叉梯度约束的联合反演方法相比，基于正弦相关约束的联合反演方法具有良好的性能。