Scientific Research

Our program focuses on technological innovation needed to create a resilient mineral supply chain to achieve clean renewable energy. Our program will also develop new pathways in the Mineral-Energy nexus, such as geothermal energy and renewable energy resources that enable a decarbonized mineral supply chain. Our work focuses on decision making under uncertainty at all levels of the Mineral-Energy nexus.

Since 2019, we have been with Kobold Metals in the area of Mineral Exploration & Resource Definition, such as

• Canada Cape Smith Massive sulfides (Ni-Co-Cu)
• Canada Crystal Lake Intrusion
• Western Australia Lithium & Sulfides
• Zambia Copperbelt (Ni-Co-Cu)

And featured in national & international media outlets

• Stanford Earth: how better mineral exploration makes better batteries 
• Stanford Human-Centered AI institute: Building Intelligent Agents to Reach Net-zero 2050
• MIT: the big tech quest to find the metals needed for the energy overhaul 
• Wired: these mining algorithms are hunting for an EV battery mother lode
• Bloomberg: future of energy requires cleaner electric batteries to solve cobalt problem
• Vanity Fair: the billionaire clubs run on cobalt says everything about our battery powered future 

Intelligent agents for sequential decision making

Building a secure, resilient and decarbonized supply chain of critical minerals that power the renewable energy future requires planning over a long time horizon. Mineral-X pioneered the use of Intelligent Agents for sequential decision making towards net-zero 2050 goals. Mineral-X developed the intelligent prospector, an Intelligent Agent (IA) which helps exploration and mining companies in decision making for optimal data acquisition and engineering operations. An intelligent agent is an AI system that can optimize goals over long-term horizons, accounting for how future information will inform system variables to improve the present decision. This general formulation has also been applied on Carbon Capture and storage and Geothermal Energy in collaboration with OMV. We are looking to start two new projects this year.

• Optimal decision making for decarbonizing the mineral supply chain
• Intelligent design of secure, resilient and just mineral supply chain systems

Publications on AI for decision making

• Caers, J., Scheidt, C., Yin, Z., Wang, L., Mukerji, T. and House, K., 2022. Efficacy of Information in Mineral Exploration Drilling. Natural Resources Research, pp.1-17.
• Hall, T., Scheidt, C., Wang, L., Yin, Z., Mukerji, T. and Caers, J., 2022. Sequential Value of Information for Subsurface Exploration Drilling. Natural Resources Research, 31(5), pp.2413-2434.
• Wang, Y., Zechner, M., Mern, J.M., Kochenderfer, M.J. and Caers, J.K., 2022. A sequential decision-making framework with uncertainty quantification for groundwater management. Advances in Water Resources, 166, p.104266.
• Corso, A., Wang, Y., Zechner, M., Caers, J. and Kochenderfer, M.J., 2022. A POMDP Model for Safe Geological Carbon Sequestration. arXiv preprint arXiv:2212.00669. Slides + Video.
• Mern, J. and Caers, J., 2023. The Intelligent prospector v1. 0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration. Geoscientific Model Development, Geoscientific Model Development, 16(1), pp.289-313.
• Wang, Y, Zechner, M., Wen, G., Corso, A.L., Mern, J. Kochenderfer, M.J. and Caers, J., 2023. Optimizing carbon storage for long-term safety, arXiv:2304.09352. 

Data Science for the Geosciences

Wang, L, Zhen Y., and Caers, J., 2023. Data Science for the Geosciences, Cambridge University Press.

Mineral-X has 25 years of experience in developing data science methods with a focus on natural resources. We build on this strength by developing methodologies in the areas of geostatistics, 3D geological modeling, geochemistry, geophysical inversion, spatial data analysis & spatially-aware machine learning.

Topics of ongoing research but not limited to are

• Optimal data acquisition through Bayesian optimal design
• Turning qualitative geological hypothesis into quantitative Bayesian prior models
• Monte Carlo approaches to uncertainty quantification in geophysical inversion
• Accelerating multi-physics geophysical inversion through machine-learning based models
• Developing state-of-the art multivariate analysis to understand the processes revealed by geochemical soil surveys.
• Stochastic level set methodologies for modeling intrusion, stratigraphy in complex structural settings

The following publications cover ongoing work in this area

• Wang, J., Zuo, R. and Caers, J., 2017. Discovering geochemical patterns by factor-based cluster analysis. Journal of Geochemical Exploration, 181, pp.106-115.
• Scheidt, C., Li, L. and Caers, J. eds., 2018. Quantifying uncertainty in subsurface systems (Vol. 236). John Wiley & Sons.
• Wang, Z., Yin, Z., Caers, J. and Zuo, R., 2020. A Monte Carlo-based framework for risk-return analysis in mineral prospectivity mapping. Geoscience Frontiers, 11(6), pp.2297-2308.
• Fouedjio, F., Scheidt, C., Yang, L., Achtziger-Zupančič, P. and Caers, J., 2021. A geostatistical implicit modeling framework for uncertainty quantification of 3D geo-domain boundaries: Application to lithological domains from a porphyry copper deposit. Computers & Geosciences, 157, p.104931.
• Yang, L., Achtziger-Zupančič, P. and Caers, J., 2021. 3D modeling of large-scale geological structures by linear combinations of implicit functions: Application to a large banded iron formation. Natural Resources Research, 30(5), pp.3139-3163.
• Athens, N. and Caers, J., 2022. Stochastic inversion of gravity data accounting for structural uncertainty. Mathematical Geosciences, 54(2), pp.413-436.