3.2 Decision-Making AI: Rare but Impactful While predictive AI is increasingly applied, decision-making AI remains rare in exploration. The most significant real-world example is the characterization of the Mingomba copper deposit in Zambia (Dempsey, 2024), where a Partially Observable Markov Decision Process (POMDP) was used to sequentially plan borehole campaigns. The explicit objective was to first falsify geological hypotheses about high-grade mineralization geometry before targeting grade and tonnage. This proved substantially more efficient than conventional grid-based drilling (Mern and Caers, 2023). The rarity of such applications is attributed to the fact that decision-making requires irrevocable resource commitments, something outside the comfort zone of academic research—and to the short-term incentive structures that dominate junior mining finance. 4. Part III — The Future of AI in Mineral Exploration 4.1 Human-in-the-Loop Data Science AI will be that of augmentation, not replacement. AI should remove tasks that humans perform poorly and amplify what they do well. Humans excel at generating process-based conceptual hypotheses, interpreting spatial relationships between geological units, and building geologically plausible models. They perform poorly at synthesizing large document sets, discovering patterns in high-dimensional data, avoiding cognitive bias, and quantifying uncertainty rigorously. The ideal system is one where AI continuously interacts with domain experts to generate, test, and update multiple competing geological hypotheses, outputting results in a decision-ready format. High-priority AI tools include: stochastic multi-physics inversion, generative AI for realistic 3D geological modeling, high-dimensional anomaly detection, surrogate models to accelerate forward simulations, and data science methods designed around falsification rather than data fitting. 4.2 Optimal Sequential Data Acquisition The Popper-Bayes framework provides a principled basis for deciding what data to acquire next. Two quantitative metrics can be used for ranking acquisition campaigns: • Value of Information (VOI): ranks surveys by expected economic return. • Efficacy of Information (EOI): a dollar-free metric quantifying how much a proposed survey design will reduce uncertainty about a target quantity, on average, across all possible data outcomes. When combined with POMDP frameworks solved by Monte Carlo Tree Search, these enable fully automated sequential drilling plan optimization. The proposed hierarchy for sequential objectives is: first drill to falsify geological hypotheses, then to constrain deposit volume, then to estimate grade. 4.3 Barriers to Implementation Four structural barriers that currently prevent the framework from being adopted at scale: • Organizational structure: major companies are organized by discipline rather than by decision problem, with data scientists making up roughly 1% of staff. Cross-disciplinary, decision-focused teams are needed.
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