Two-Dimensional Joint Inversion of DC Resistivity and Magnetic Data for Archaeological Investigation

Magnetic data inversion excels in identifying surface magnetic anomalies, yet it lacks crucial depth information. On the other hand, direct current resistivity (DC resistivity) measurements can provide more detailed depth information with high vertical resolution. This study focuses on the joint inversion DC resistivity and magnetic data, which combines complementary information to establish a more robust geological model. This method addresses the inconsistencies inherent in separate inversions, thereby enhancing the resolution, stability and interpretability of geological features. We develop a novel joint inversion cost function for DC resistivity and magnetic data. This cost function leverages a model parameter transformation function to bridge the significant discrepancies between resistivity and magnetic susceptibility, thereby offering practical advantages in enhancing the inversion model's accuracy. By incorporating cross-gradient and fuzzy C-means (FCM) clustering, we enhance the coupling between inversion parameters and further improve the efficacy of joint inversion. Theoretical and field data inversion results demonstrate that the hybrid constraints, combining cross-gradient and FCM clustering, notably enhance the inversion performance of the magnetic susceptibility model. This method is capable of effectively recovering boundary anomalies and physical property values, which also resolves the inconsistencies that are often encountered in separate inversions.