mapping (Carranza & Laborte, 2015; Cracknell & Reading, 2014) as well as to more direct threedimensional geological modeling and uncertainty analysis at the prospect and camp scales (Jessell et al., 2018). Uncertainty modeling addresses the central objective of exploration: reducing uncertainty about the subsurface in order to support better decisions. To enable rigorous optimization and comparison of alternative exploration strategies, uncertainty must be explicitly represented rather than treated implicitly or qualitatively. Modern AI approaches to uncertainty modeling—often grouped under probabilistic modeling—include methods such as normalizing flows, deep generative models, variational latent variable models, and Bayesian neural networks (Rezende & Mohamed, 2015; Kingma & Welling, 2014; Neal, 2012). These methods are designed to operate in highdimensional, uncertain domains and provide not only predictions but also calibrated measures of confidence. When applied to subsurface problems, they enable the generation of three-dimensional uncertainty maps and ensembles of plausible geological realizations, supporting both interpretation and downstream decision-making. Decision support and optimization build on fused data representations and probabilistic models to guide exploration actions under uncertainty. In many high-stakes domains—such as robotics and autonomous systems—AI models are explicitly embedded within closed-loop decision frameworks that combine learning, simulation, and active planning. Methods such as reinforcement learning and Monte Carlo tree search are used to evaluate large sets of possible actions and future states, optimizing long-term objectives rather than single-step criteria (Sutton & Barto, 2018; Browne et al., 2012). In exploration, analogous decision problems arise when selecting drill targets, survey designs, or sequencing strategies under uncertain geology. Formal results in Bayesian decision theory demonstrate that closed-loop, adaptive strategies can outperform open-loop or one-shot approaches—such as static information-theoretic targeting— both in expected value and regret bounds (Frazier, Powell, & Dayanik, 2008). As a result, AIenabled decision optimization represents a critical step toward scalable, repeatable exploration decision-making. 2.2 Trust and Validation For AI systems to be adopted responsibly in exploration, trust and interpretability are essential. Exploration decisions are capital-intensive and therefore require not only predictive performance but also clear understanding how AI outputs are generated. Without appropriate transparency and validation, even technically strong AI models risk being underutilized or misapplied. A foundational requirement for trust is clarity about the underlying methodology. AI providers, in most cases, should be able to explain what class of methods is being used, what classes they are training, and how those methods differ from established techniques beyond. In practice, AI developers should be able to share a fair amount of clarifying detail without unreasonable IP risk. This transparency can help distinguish AI approaches from the rebranding of long-established data science or geostatistical methods—such as kriging or principal component analysis—as “AI.” At the same time, it is important to recognize that some degree of black-box behavior is unavoidable in modern AI systems. State-of-the-art neural models commonly contain hundreds of millions to billions of parameters and rely on deeply non-linear transformations. Unlike traditional
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