38 EM survey data. These revealed that the intrusion could take many plausible shapes, far beyond what a single deterministic inversion would suggest, and fed directly into AI-assisted drilling planning. 2.4. Popperian Falsification Karl Popper’s philosophy holds that scientific theories should be tested by attempting to falsify them, not confirm them. Applied to mineral exploration, this means data acquisition should first aim to falsify geological hypotheses, not merely detect mineralization. Bayes and Popper can be combined into a ‘PopperBayes’ protocol: • Domain geologists generate multiple conceptual model hypotheses from existing data. • Numerical modelers translate these into mathematical representations with quantified uncertainty. • Prior distributions are tested against observed data through falsification (statistical tests). • Bayesian inversion integrates surviving hypotheses with new data. • Decisions on further data acquisition are made from the resulting uncertainty quantification. Executing this protocol requires all domain experts—geologists, geophysicists, geochemists, geostatisticians, and decision scientists—to work in an integrated workflow, something that does not reflect how exploration companies are currently organized. 3. PART II — CURRENT AI APPLICATIONS AND COMMON ERRORS 3.1. Predictive AI: The Data Curation Problem The most critical challenge in applying machine learning to mineral exploration is not algorithm selection—it is dataset curation. Three pervasive errors that generate false positives: • Scale mismatch: core samples (centimeter-scale) are compared with geophysical data (tens to hundreds of meters), artificially inflating apparent correlations. • Deterministic interpolation: interpolating geophysical and geochemical data deterministically reduces variance, smoothing out variability that is essential for accurate predictions. Stochastic simulation should be used instead. • Unjustified data pooling: merging data from geologically distinct zones produces spurious correlations. Models trained in one area cannot be transferred to another without explicit testing. A prospectivity mapping case study (Wang et al., 2020) quantifies the difference: deterministic interpolation yielded a prediction recall of 0.4 against known deposits; stochastic simulation raised it to 0.7. Stochastic outputs also enable risk-return analysis—identifying zones with high expected value alongside manageable uncertainty. 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.
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