Joint Inversion of Potential Field Data with Adaptive Unstructured Tetrahedral Mesh

Abstract

Inverting potential field data presents a significant challenge due to its ill-posed nature, often leading to non-unique model solutions. Addressing this, our work focuses on developing a robust joint inversion method for potential field data, aiming to achieve more accurate density and magnetic susceptibility distributions. Unlike most previous work that utilizes regular meshes, our approach adopts an adaptive unstructured tetrahedral mesh, offering enhanced capabilities in handling the inverse problem of potential field methods. During inversion, the tetrahedral mesh is refined in response to the model update rate. We integrate a Gramian constraint into the objective function, allowing enforcement of model similarity in terms of either model parameters or their spatial gradients on an unstructured mesh. Additionally, we employ the moving least-squares method for gradient operator computation, essential for model regularization. Our model studies indicate that this method effectively inverts potential field data, yielding reliable subsurface density and magnetic susceptibility distributions. The joint inversion approach, compared to individual dataset inversion, produces coherent geophysical models with enhanced correlations. Notably, it significantly mitigates the non-uniqueness problem, with the recovered anomaly locations aligning more closely with actual ground truths. Applying our methodology and algorithm to field data from the Ring of Fire area in Canada, the joint inversion process has generated comprehensive geophysical models with robust correlations, offering potential benefits for mineral exploration in the region.