3D Aeromagnetic Inversion Using Unsupervised Deep Learning: Imaging Deep Magnetic Structures in the Panxi Region, SW China

Panzhihua-type V–Ti magnetite deposits in the Panxi region are hosted in mafic–ultramafic intrusions, and their exploration potential depends strongly on the deep distribution of ore-bearing intrusions. High-resolution 3D magnetic inversion is an effective tool to image the geometry of these intrusions. Using 1:50,000 aeromagnetic data, we applied an unsupervised deep learning inversion to obtain the 3D magnetic susceptibility structure of related intrusions. The results show that magnetic anomalies are mainly NS and NEE trending, with minor NNW-trending features. NS-trending sources occur in the Baima–Miyi–Hongge zone between the Xigeda–Yuanmou and Anninghe faults, while NEE-trending anomalies lie west of the Xigeda–Yuanmou fault and east of the Chenghai fault. Integrated geological analysis reveals two Late Variscan rift systems: the Anninghe rift and the Panzhihua rift. Deep fault-controlled magma ascent and emplacement, forming the Emeishan large igneous province, are associated with strongly magnetic intrusions. Mantle plume-derived magmas, differentiated in shallow and deep magma chambers, generate well-differentiated layered complexes at depths < 10 km with magnetic intensities of 5–10 A/m. Shear structures within paleorifts provide favorable emplacement conditions and controlled ore localization. We propose a three-in-one ore-controlling mechanism involving rift systems, intrusive rocks, and shear structures for Panzhihua-type V–Ti magnetite mineralization.