Toward Robust Three-Dimensional Magnetic and Gravity Inversion Using Deep Learning

Magnetic and gravity surveys remain among the most cost-effective geophysical tools for investigating the subsurface. They provide information on rock geometry and bulk properties at regional to deposit scale, and they have long been used to guide mineral exploration. However, turning geophysical anomalies into reliable three-dimensional property models requires inversion, a process that is inherently non-unique: multiple subsurface distributions can explain the same anomaly. Conventional approaches, such as least-squares or Bayesian inversion, can produce valuable results; however, they remain computationally demanding for large 3D models and require strong regularization choices that may bias geological interpretation.
Over the last decade, geoscientists have explored machine learning as an alternative approach. Instead of repeatedly solving forward equations, machine learning methods learn a mapping between geophysical anomalies and subsurface properties using large training libraries of synthetic examples. Early work with convolutional neural networks (CNNs) and U-Net architectures showed the concept is viable for electromagnetic and seismic data. More recent studies have shown that deep neural networks can recover magnetic susceptibility distributions from magnetic data and, in some cases, perform joint inversion of gravity and magnetic observations. Nevertheless, purely convolutional architectures often struggle to preserve long-range spatial relationships in fully three-dimensional volumes, resulting in blurred boundaries and reduced geological interpretability.
Recent advances in deep learning offer new opportunities to address these limitations. Emerging models are designed to capture long-range dependencies and preserve sharper boundaries. They have been effective in other 3D volumetric fields, such as medical imaging and seismic interpretation, but have yet to be explored for potential-field inversion.
In this study, we develop a deep-learning-based inversion method for magnetic and gravity data aimed at critical mineral exploration. The approach targets mineral systems with distinct geophysical signatures, with a focus on volcanogenic massive sulfide (VMS) environments. By combining data-driven learning with physics-informed training, the method produces reproducible three-dimensional susceptibility and density models that reduce ambiguity in subsurface interpretation. The workflow is tested using data from the Flin Flon VMS district in Manitoba, Canada, demonstrating its potential to improve targeting of buried copper-zinc mineralization and to support the integration of advanced AI methods into geoscience workflows.