Bridging Geoscience and Data Science for Smart Mineral Exploration

Climate action goals, such as achieving net-zero emissions, heavily depend on scaling up renewable energy systems, which require a reliable supply of critical minerals and metals. Projected deficits of these resources underscore the urgent need to discover new, economically viable deposits. However, conventional practices of mineral exploration are slow, expensive, risky, heuristic and unsustainable. In this talk, I’ll share how mineral exploration can benefit from joint efforts between geologists and data scientists working together, combining field experience with computational tools. I will outline the motivation behind using AI to improve discovery rates, reduce costs, and limit environmental impact. Through real-world case studies, we will see how machine learning methods applied to geophysical and geochemical data help reveal hidden patterns in datasets, while geoscientists guide model development and interpretation. Central to our approach is a human-in-the-loop, explainable AI workflow: We build mutual understanding with geoscientists and those experts review and refine model outputs, ensuring that geological intuition and local knowledge shape every step. We will discuss how we handle uncertainties by integrating statistical analysis with on-site validation and expert judgment. Key challenges in interdisciplinary work, such as building trust in AI results, and maintaining clear communication, will be presented alongside practical solutions like iterative feedback sessions.

Speaker: Adel Asadi, Stanford University