SC-04: Probabilistic Deep Learning Inversion for Critical Mineral Exploration

Graduate students and professionals who want to apply latest generative AI models to critical mineral exploration problems.

This two-day short course introduces geoscientists to the latest deep generative models for solving geophysical inverse problems in critical 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, speed, and uncertainty quantification in inversion workflows. By the end, attendees will be equipped to integrate deep generative AI into real-world exploration projects.

1. Equip participants with practical knowledge and skills to use deep generative AI in critical mineral exploration.
2. Enable participants to implement four different deep generative models (GANs, INNs, cVAEs, NFs) for solving geophysical inverse problems in mineral exploration setting.
3. Grow the mining community within SEG and attract more mining professionals to IMAGE '26.
4. Bring mining industry up to speed with the latest deep generative AI methods.