Artificial Intelligence in Energy Transition Mineral Exploration

This review explores the role of Artificial Intelligence (AI) in critical mineral exploration, examining key challenges and opportunities. Global decarbonisation efforts are driving a significant increase in the demand for critical minerals, intensifying the need to find new deposits, improve operational efficiencies and diversify global mineral supply capabilities. Challenges encountered in critical mineral exploration, such as increasingly complex geological settings in new exploration, declining ore grades and the need for sustainable practices, are calling for a more comprehensive analysis of a diverse set of aspects, including large geoscientific and economic datasets, as well as Environmental, Social and Governance (ESG) considerations. Traditional methods of analysing such extensive information are time-intensive, expensive and often result in lower predictability. This, combined with the growing need for faster and more accurate decision-making, has driven the adoption of innovative approaches such as AI-based techniques that bring vast amounts of multifaceted information into the analysis. Integrating AI applications in the exploration of critical minerals is bringing rapid transformation to the sector, driving new trends in the market dynamics. AI methodologies, Machine Learning (ML) and AI-enabled data analytics are improving the predictive modelling and automation of exploration studies and allowing enhanced decision-making capabilities. The shift towards data-driven strategies allows the use of both new and previously underutilised datasets owned by the companies, leading to better assessments of the project viability, more effective and rapid evaluation of a wide range of datasets for exploration potential and identification of opportunities for cost reduction. These advancements influence risk identification and management, bring strategic shifts in the critical minerals market and global supply chains and offer valuable insights to stakeholders shaping investment patterns. Challenges in the application of AI in critical mineral exploration include data requirements, technical complexity and ethical and regulatory concerns. Recognising AI’s vast potential, extensive research and development efforts are underway, focussing on refining AI algorithms, developing novel approaches to integrate with geoscientific data, promoting collaboration between geoscientists and AI experts and addressing the ethical, regulatory and ESG considerations.