lithology and faults were converted into continuous representations to enable integration into the predictive model. Fuzzy Logic and Machine Learning were selected for their ability to integrate multiple variables from different sources, including geology, geochemistry, topography, surface alteration, and slope, while combining bibliographic information with expert knowledge. Fuzzy Logic enabled manual adjustment of the relative weights of variables, incorporating hypotheses and prior experience into the design of the models, and provided flexibility to analyze related deposits and transitional zones, revealing patterns specific to the study area. The methodological workflow included data preprocessing, targeted transformations, sensitivity tests, systematic documentation of changes, and continuous validation using the F1-Score to evaluate the accuracy of the prospectivity map and balance false positives and false negatives. The core algorithm was designed specifically for the deposit types of interest, assigning relative weights to variables according to their relevance, generating fuzzy memberships that highlight areas with a high probability of hosting mineralization. The variables were then combined using Fuzzy Logic to derive the Mineral Prospectivity Map (MPM). (figure 4) The methodology also integrated passive sensors such as ASTER and WorldView with geochemical, geophysical, and topographic data. This integration helped overcome challenges such as dense vegetation and complex terrain, and improved the spatial accuracy of predictions, supporting a scalable and transferable workflow that can be applied to other metallogenic provinces. 4. RESULTS, OUTCOMES, AND PERFORMANCE The predictive analysis showed that zones with a higher number of overlapping favorable layers consistently obtained higher scores, reinforcing their prospectivity. However, isolated occurrences of certain elements or evidential layers, although associated with mineral deposits, produced some false positives in areas with limited supporting data. The model also demonstrated that individual variables can identify localized prospective areas, while Fuzzy Logic tends to emphasize deposit-related patterns in a more diffuse way, delineating zones with lower individual probability but stronger consistency across multiple key variables. In most cases, areas of high predicted probability coincided with known deposits, although some deposits showed lower probability because they were subordinated to the main deposit type that the algorithm was designed to model. This underscores the importance of careful algorithm design to capture the specific characteristics of each district. The methodology helped overcome exploration challenges related to dense vegetation by improving the correlation between predictions and field observations. In collaboration with IAMGOLD Perú, priority zones within the study area were identified and validated through sampling and spectral analysis, although detailed results remain confidential. The same approach was also applied to a lithium exploration project targeting pegmatites in the Pontiac Group in Abitibi, Canada, where radiometric geophysics successfully mapped targets beneath dense forest cover and showed excellent correlation with field data, demonstrating the transferability of the method. 200
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