Although various magnetic inversion techniques have been developed in geophysics, traditional methods are often constrained by inherent limitations such as low computational efficiency and pronounced non-uniqueness. In 3D magnetic inversion, multi-dimensional deep learning methods have shown promise in numerical simulations; however, their generalization capabilities and practical effectiveness in real-world geological applications, particularly in complex settings like gold exploration, remain underexplored. This study introduces MAGNETPRO, a residual convolutional neural network based on an encoder–decoder architecture, designed to accurately invert 2D magnetic field data into 3D magnetic susceptibility structures. To enhance the model's generalization ability and inversion accuracy, an innovative data construction strategy was implemented to create a highly randomized training dataset incorporating complex geological features. Theoretical model tests demonstrate that MAGNETPRO achieves inversion accuracies of 97% across the entire region and 80% within magnetic structure areas, highlighting its excellent spatial resolution and structural recognition capabilities. To further validate its practical effectiveness, the method was applied to real exploration data from a gold mining area in Fujian Province. The results show a high degree of consistency between the inversion outcomes and drilling data, confirming the method's reliability and practical value under real geological conditions. [ABSTRACT FROM AUTHOR]

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