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
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