the term AI specifically means, what methods it refers to, and generally what its capabilities (and limitations) are. Developing a deeper understanding of these questions is critical for leaders in the minerals world to be able to meaningfully assess which new AI technologies to adopt, and to build the trust and verification methods necessary to deploy them correctly in their operations. In this paper, we focus on AI applications in exploration. Exploration is inherently difficult due to extreme data sparsity, high geological uncertainty, non-unique interpretations of indirect geophysical measurements, and long feedback cycles between decisions and outcomes. Critical decisions are often made early, when information is incomplete and heterogeneous, yet these decisions disproportionately determine 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, sitespecific 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-theshelf 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
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