2.75-D Global joint inversion of gravity and magnetic anomalies with appraisal of model reconstruction uncertainty

Abstract

Inversion procedures are fundamental tools for reconstructing causative sources of gravity and magnetic anomalies. While 2-D polygonal and 3-D polyhedral models can represent irregularly shaped bodies, a more flexible framework is needed to bridge the gap between computational simplicity and geometric realism. To address this, we propose a 2.75-D global joint inversion scheme based on the nature-inspired Hunger Games Search (HGS) metaheuristic algorithm. Synthetic tests involving modal and sensitivity analyses were carried out to identify potential difficulties and uncertainties in the considered problem, revealing that the global optimizer must efficiently balance the critical trade-off between global exploration and local exploitation. The proposed scheme was applied to synthetic anomalies, with and without noise, and benchmarked against the probabilistic Hamiltonian Monte Carlo algorithm. Two field datasets were then analyzed, and the results were interpreted considering existing geological and geophysical knowledge. Post-inversion analyses confirmed the reliability of the estimated models, while compact inversion and correlation imaging techniques supported the HGS outcomes. Notably, joint inversion consistently improved convergence and reduced estimation errors compared to individual inversions by exploiting the complementary sensitivities of gravity and magnetic data. The 2.75-D approach enhances geometric flexibility while maintaining a parsimonious parameterization. HGS is an efficient and robust optimizer for joint inversion of gravity and magnetic anomalies, capable of producing geologically plausible models with rapid convergence and minimal uncertainty.