Korzhinskii-Net: Physics-Informed Neural Network for Sub-Surface Mineral Prospectivity Modelling
Researchers have developed Korzhinskii-Net, a novel physics-informed neural network designed for mineral prospectivity modeling. This 2D radial PINN integrates physical principles like fluid flow and heat transport into a differentiable forward model, supervised by surface data. Tested across five diverse ore provinces, Korzhinskii-Net significantly outperformed classical machine learning baselines, demonstrating its ability to identify subsurface ore localization patterns that purely data-driven methods miss. AI
IMPACT This physics-informed approach could enable more accurate subsurface exploration, reducing costs and improving discovery rates in resource-rich regions.