The processing of potential field datasets requires many steps; one of them is the inverse modeling of potential field data. Using a measurement dataset, the purpose is to evaluate the physical and geometric properties of an unidentified model in the subsurface. Because of the ill-posedness of the inverse problem, the determination of an acceptable solution requires the imposition of a regularization term to stabilize the inversion process. We also need a regularization parameter that determines the comparative weights of the stabilization and data fit terms. This work offers an evaluation of automated strategies for the estimation of the regularization parameter for underdetermined linear inverse problems. We look at the methods of generalized cross validation, active constraint balancing (ACB), the discrepancy principle, and the unbiased predictive risk estimator. It has been shown that the ACB technique is superior by applying the algorithms to both synthetic data and field data, which produces density models that are representative of real structures and demonstrate the method’s supremacy. Data acquired over the chromite deposit in Camaguey, Cuba, are utilized to corroborate the procedures for the inversion of experimental data. The findings gathered from the three-dimensional inversion of gravity data from this region demonstrate that the ACB approach gives appropriate estimations of anomalous density structures and depth resolution inside the subsurface.
