There are at least two preferential orientations for the emplacement of mineralized structures within the district (ENE and NE). The study enhanced the understanding of the data through the generation of graphical analyses of the variables. Additionally, it improved the comprehension of how to define new zones through the generation of explainability graphs, providing valuable information to the exploration team for extending studies in the district. These new knowledge parameters allow for more informed decisions about which tools to extend or implement in the future, thereby maximizing the use of budgets. The study successfully identified extensions of known mineralized areas and new zones to explore, consolidating the application of the methodology and technology as an additional layer of passive information that can be used strategically for defining future areas of interest. The primary metric used to select the best ML architecture was precision, over recall, F1-score, accuracy, and specificity. This was done with the objective of minimizing the presence of false positives in the models. A key aspect to highlight in this work is that, despite the relatively low recall and precision, the fundamental goal in the context of mineral exploration is the ability to identify mineralized bodies. In this case, it was not necessary to achieve millimeter-level precision, as small displacements of a few meters in the estimated location do not affect the practical value of the model. The important thing was to provide a reliable guide to focus efforts on areas with higher potential, thus optimizing resources and time in the early stages of the predictive model exploration process Figure 3B. ACKNOWLEDGEMENTS The authors thank First Majestic for permission to present this case study and the technical team for their support in developing these predictive models. REFERENCES Albrecht, T., González, I., & Klump, J. (2021). Using Machine Learning to Map Western Australian Landscapes for Mineral Exploration. MDPI, v. 10. Mendoza, R., Merino, R., Vasquez, M., & Rosario, P. (2020). NI 43-101 Technical Report, San Dimas Silver/Gold Mine, Durango and Sinaloa States, Mexico.
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