Keywords Machine Learning; Fuzzy Logic; mineral prospectivity mapping; remote sensing; predictive analysis; Cajamarca; Peru. 1. CONTEXT AND PROBLEM STATEMENT Mineral resources are essential for economic development, but the discovery of new deposits has declined, especially for large deposits that host most of the world’s metal production. To address this challenge, exploration strategies must adopt new paradigms and focus on underexplored areas, while managing geological uncertainty and increasing social and environmental expectations. Within the theme of the World Mining Congress 2026, the mining industry must deliver the minerals society urgently needs faster, smarter, and more responsibly, while building trust with communities and stakeholders. Geomatic World Inc. has developed advanced algorithms and predictive models based on artificial intelligence (AI) that leverage geospatial data to identify areas with high mineral exploration potential. Predictive analysis integrates geological, geochemical, and geophysical information (figure 3) to support decision-making and reduce the inherent risk of early-stage exploration. The Cajamarca region of northern Peru hosts many world-class porphyry copper and high-sulfidation epithermal deposits, making it an ideal natural laboratory to test AI-based mineral prospectivity workflows. (figure 1) 2. OBJECTIVES AND SCOPE This project pursued three main objectives: (i) to demonstrate the usefulness and capabilities of AI-assisted remote-sensing interpretation and predictive analysis for the exploration and characterization of porphyry copper and high-sulfidation epithermal systems; (ii) to perform an integrated predictive analysis that combines remote-sensing interpretation, regional geology, structural data, geochemistry, and geophysics to build mineral prospectivity models; and (iii) to generate a Mineral Prospectivity Map (MPM) (figure 2) as a key tool to guide future exploration, optimize resource allocation, and provide a solid basis for continued mineral exploration in the region. The work directly addresses the WMC 2026 themes of delivering the minerals society urgently needs and getting more from what we already have by improving exploration targeting, shortening timelines to discovery, and supporting more efficient and responsible decision-making at the regional scale. 3. METHODOLOGY OR APPROACH The analysis began with extensive preprocessing of datasets from multiple sources, including geochemical measurements, geophysical surveys, and satellite imagery.(figure3) Data cleaning removed erroneous or missing values to ensure quality. Continuous variables were transformed, where appropriate, using logarithmic and exponential functions, followed by normalization to allow consistent comparison between heterogeneous datasets. Discrete variables such as 199
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