47 4. CONCLUSIONS AND FUTURE WORK This paper examined the application of AI to mineral exploration, with a focus on data fusion, uncertainty modeling, and decision optimization, and demonstrated their impact through a large-scale copper porphyry case study. The results show that AI-driven, probabilistic subsurface modeling can produce materially more precise resource estimates than conventional expert-based uncertainty analyses, while maintaining geological plausibility and consistency with available data. In addition, sequential, AI-guided drilling optimization achieved resource models statistically comparable to those derived from conventional drilling campaigns using approximately 45% of the drilling meters, indicating substantial potential for accelerating resource characterization and reducing exploration cost and risk. At the system level, these improvements translate into meaningful implications for project economics, development timelines, and long-term copper supply constraints. Despite these encouraging results, AI in exploration remains at a relatively early stage of maturity. Subsurface systems are complex, data are sparse and heterogeneous, and outcomes are strongly conditioned by geological context. As a result, realizing the full potential of AI will require continued bi-directional learning between the AI research community and the resource sector. Advances in model architecture, uncertainty quantification, and decision frameworks must be informed by geological realism and operational constraints, while domain practices must evolve to effectively integrate probabilistic outputs and adaptive decision-making into established workflows. Future work spans several important directions. At regional scales, two-dimensional prospectivity mapping remains an active area of research, with opportunities to improve robustness, generalization across geological settings, and validation methodologies. At the project scale, significant potential exists to extract additional information from geophysical data by applying AI directly to raw or minimally processed measurements, rather than relying primarily on derived or interpreted products as inputs. More broadly, extending AI-driven frameworks to additional exploration problems—such as survey design, adaptive data acquisition, and integrated economic decision-making—represents a promising avenue for further impact. Continued progress across these fronts will be critical to transitioning AI from promising pilot applications to a scalable, trusted component of modern exploration practice. ACKNOWLEDGEMENTS The author would like to thank the complete team at Terra AI, advisors and past collaborators at Stanford University, and our entire set of industry partners for providing insights and experiences inspiring this work.
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