42 project cost, risk, and ultimate success. These characteristics make exploration a natural application area for AI systems that can systematically integrate diverse data types, quantify uncertainty, and support consistent decision-making under uncertainty. 2. AI METHODS IN EXPLORATION AI has been applied across a wide range of exploration tasks, spanning sample-scale geochemical analysis through to basin- and district-scale structural interpretation. Within industry practice, adoption has largely concentrated in two application areas. The first is discovery identification, where AI methods are applied primarily in 2D maps over large spatial extents to identify prospective regions for follow-up exploration and testing (e.g., VRIFY’s AI-assisted discovery workflows; VRIFY, 2025). While promising, these approaches face long validation timelines due to the inherently stochastic and sparse nature of mineral discovery — even substantial improvements in targeting efficiency can still correspond to low discovery ratios in practice — and because regional-scale applications must contend with strongly non-stationary relationships between geophysical responses and underlying geology that challenge even modern supervised learning methods. The second area is exploration and modeling, which focuses on understanding mineral systems at the prospect- to camp-scale, typically in three dimensions and with richer, site-specific datasets that help overcome some transfer limitations of broad 2D targeting. This domain remains more nascent and generally requires highly customized workflows rather than off-the-shelf models, but offers greater potential to integrate uncertainty-aware modeling and geological constraints (Caers, 2025; Mineral-X, 2025). We explore this latter area in greater detail in the following sections. 2.1. Key Capabilities in Exploration Across exploration and subsurface modeling workflows, three underlying AI functions have emerged as particularly impactful: data fusion, uncertainty modeling, and decision optimization. Together, these capabilities address key structural limitations of conventional exploration workflows and enable more systematic reasoning under uncertainty. Data fusion refers to the joint analysis of multiple heterogeneous data sources—such as geophysics, geochemistry, geological mapping, and drilling data—within a single modeling framework. A central limitation of traditional exploration practice is that these data types are typically analyzed using separate, loosely coupled quantitative processes, often requiring subjective reconciliation at later stages. AI-based data fusion methods enable the simultaneous analysis of these data, allowing models to capture higher-order interactions and non-linear relationships that are difficult to represent using manual or sequential approaches. In mineral exploration, fused data representations have been successfully applied to regional prospectivity mapping (Carranza & Laborte, 2015; Cracknell & Reading, 2014) as well as to more direct three-dimensional 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 high-dimensional, 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
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