Enhancement‐Constrained Parsimonious Stochastic Magnetic Inversion Framework for UXO Localization

ABSTRACT Magnetic surveying is widely used for unexploded ordnance (UXO) detection. However, reliable target localization and characterization remain challenging because of noise, orientation‐dependent magnetic responses, elongated target geometries and inversion non‐uniqueness. In this context, we present an enhancement‐constrained stochastic magnetic inversion workflow that uses anomaly‐enhancement results as spatial prior information to restrict the admissible model space before optimization. First, magnetic anomalies are denoised using a modified non‐local means approach to suppress high‐frequency noise that would otherwise be amplified during derivative‐based processing. Modified local‐phase filters are then used to delineate laterally confined target zones, whereas approximate depth estimates constrain the vertical search range. The resulting spatial bounds are incorporated into a parsimonious stochastic inversion based on the Hunger Games Search algorithm, in which UXO responses are approximated by uniformly magnetized equivalent spheres for computational efficiency. The workflow is tested using noisy synthetic data generated from elongated UXO‐like sphero‐cylinders. The intentional mismatch between the forward‐model geometry and the equivalent‐sphere inversion model provides a more demanding assessment of robustness to geometric simplification. Normalized source strength (NSS)‐based Euler deconvolution (NSS‐Euler) is evaluated as an independent localization benchmark and is not incorporated into the proposed inversion workflow. The proposed framework is then applied to magnetic data acquired over targets with known positions and orientations at a controlled test site in Slovakia. Compared with otherwise identical unconstrained inversions, the derived spatial constraints improve target localization, reduce solution ambiguity and increase the consistency of the recovered models. NSS‐Euler provides an independent estimate of target location and depth, whereas the constrained stochastic inversion yields more accurate localization and depth estimates while additionally recovering effective‐magnetization and other source‐related parameters. Although this simplified representation cannot reproduce all geometric details of real UXO bodies, it offers a practical and computationally efficient framework for localization‐oriented interpretation. The proposed strategy may also be applicable to other compact‐target‐detection problems in which enhancement‐derived spatial information can guide inversion.