<p>The inversion of magnetic data is a crucial geophysical technique for imaging subsurface structures, but it is inherently non-unique. Regularization is essential to obtain geologically plausible solutions, with stabilizers based on L₂ norms often producing overly smooth models that obscure boundaries, while sparse constraints can lead to artificially concentrated features with exaggerated susceptibilities. This paper introduces a novel framework for magnetic data inversion utilizing a mixed-order minimum entropy stabilizer, which integrates both first- and second-order probability measures within a pseudo-quadratic form. The proposed method is designed to mitigate the limitations of its individual components, striking a balance between the excessive focusing of first-order minimum entropy and the over-smoothing of second-order minimum entropy stabilizers. To achieve the solution, we use a reweighted regularized conjugate gradient (RRCG) algorithm as an efficient approach. The efficacy of this approach is demonstrated through comprehensive testing on two synthetic models—a dipping dike and two cuboids—and a field dataset from the Nikka volcanogenic massive sulfide (VMS) deposit in Ontario. Results show that the mixed-order stabilizer consistently generates compact models with clearly defined boundaries and accurate susceptibility values, outperforming the conventional methods. This study confirms that the mixed-order minimum entropy regularization provides a robust and effective tool for producing geologically realistic subsurface models from magnetic data, enhancing interpretation accuracy in exploration geophysics.</p>