Probabilistic Deep Learning Inversion for Critical Mineral Exploration

This short course introduces geoscientists to modern machine learning and deep generative models for solving geophysical inverse problems in mineral exploration. It begins with an overview of inverse theory and then transitions to state-of-the-art AI methods used to recover complex subsurface structures. Participants will learn the concepts and practical implementation of GANs (generative adversarial networks), cVAEs (conditional variational autoencoder), INNs (invertible neural networks), and NFs (normalizing flows). Hands-on coding exercises and case studies will demonstrate how these models improve geological realism, […]