41 AI-ENABLED MINERAL EXPLORATION: TOWARD FASTER, CHEAPER, AND SMARTER DISCOVERY *J. Mern PhD1 1Terra AI, CA, USA (*Presenting author: jmern@TerraAI.Earth) ABSTRACT Exploration decisions are made under severe geological uncertainty, sparse data, and long feedback cycles, yet early-stage outcomes disproportionately influence project cost, risk, and development timelines. This paper evaluates how modern AI methods can be applied to improve exploration decision-making by integrating heterogeneous data, explicitly modeling subsurface uncertainty, and optimizing sequential exploration actions. We present a structured assessment of AI applications in exploration, focusing on three core capabilities: multi-modal data fusion, probabilistic uncertainty modeling, and closed-loop decision optimization. We discuss methodological considerations related to transparency, interpretability, and validation that are necessary for responsible deployment in operational settings. To quantify impact, we apply these methods to a structurally complex copper porphyry system using legacy drilling, geophysical, and geological data. A neural-network-based generative modeling framework is used to produce probabilistic three-dimensional subsurface realizations and associated resource distributions, which are then coupled with stochastic drilling optimization to guide sequential targeting. The case study demonstrates that AI-derived resource models achieve substantially tighter uncertainty bounds than conventional expert-driven range analyses while maintaining geological consistency with available data. Sequential AI-guided drilling reaches resource models statistically comparable to those obtained through conventional targeting using approximately 45% of the drilling meters. We further show that improvements in early-stage resource precision can significantly reduce false-positive advancement decisions and that accelerated resource upgrading could materially affect projected copper supply trajectories. KEYWORDS World Mining Congress, Mineral Exploration; Probabilistic Modeling; Artificial Intelligence; Data Integration; Copper; Uncertainty Quantification 1. INTRODUCTION The term “AI”, an acronym for Artificial Intelligence, has recently become a focus of minerals, mining, and critical resource development across the world. Many see AI as having the potential to solve long-standing problems in the industry, ranging from exploration through production. Like in most other emerging fields of application, there is not yet a consensus understanding of what 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
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