AI in Mineral Exploration: Separating Hype from Reality Jef Caers, Stanford University, jcaers@stanford.edu Abstract The energy transition has focused global attention on critical mineral exploration, yet the industry has seen declining discovery rates despite increasing investment. This paper argues that the core problem is methodological: the absence of a rigorous, overarching scientific method for exploration. This paper proposes a framework grounded in Bayesian probability and Popperian falsification, enabled by AI, that aims to reduce cognitive bias, limit false positives, and make decision-making more rational and cost-effective. This paper is a summary of “The future of AI in mineral exploration”, which can be found on ArXiv. 1. Introduction Drawing on three decades of experience in data science and decision science applied to Earth resources, I argue that AI in mineral exploration should be seen not as a tool for autonomous discovery, but as an enabler of better scientific method. The paper contrasts the mining sector— where deterministic modeling remains the norm—with oil & gas, where Bayesian uncertainty quantification, ensemble modeling, and AI-assisted decision-making have become standard. Return on investment in exploration has been largely negative for decades. This is not due to a lack of investment or technology, but to the absence of a unifying scientific method that governs how exploration decisions are made. The paper is structured in three parts: (I) a new scientific method, (II) current AI applications and their errors, and (III) the future of AI in mineral exploration. 2. Part I — A New Scientific Method 2.1 Decision-Making in Exploration Exploration is fundamentally an exercise in reducing uncertainty about the existence and economics of ore deposits, operating across scales from continental mapping down to drilling. Decisions are defined as the irrevocable allocation of financial resources. The primary decision at each stage is: what data should we acquire next, and does it justify the cost? This question that is rarely answered with adequate rigor. 2.2 The Problem with Determinism A typical exploration campaign under cover currently proceeds by inverting geophysical data into a single 3D model, interpreting it geologically, and planning drilling based on that single model. The critical flaw is that uncertainty at every step is ignored. The result is a high rate of false positives—boreholes that intersect nothing of interest. When this happens, reasons are rarely analyzed; the project moves on to the next anomaly. 2.3 The Bayesian Approach The Bayesian framework requires explicit quantification of prior uncertainty before data acquisition. Rather than estimating a single ‘true’ model, it demands consideration of all plausible
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