2.2.4 Geophysics Includes 13 layers of magnetometry and radiometry, processed with a 30-meter grid cell size 3. METHODOLOGY The process followed two stages: 3.1. First Stage Clustering the area into 5 groups (77,198 samples). The Veta Pérez sector (22.10% of data) withheld for validation, while the remaining 77.89% was used for training. 3.1.1. Clustering The amount of available drill hole data was considered sufficient to perform clustering rather than random selection. The team conducted several preliminary tests were conducted before arriving at the final configuration. We clusters the area into five clusters, corresponding to 77,198 drill hole samples. Clusters 0, 1, 2, 3, and 4 represent 14.73%, 22.10%, 27.15%, 5.03%, and 30.99% of the total data, respectively Figure 2A. This segmentation is critical as it ensures that the model’s predictive logic is grounded in the district's specific geological domains rather than being a generalized mathematical abstraction. 3.1.2. Test Area Selection After testing several areas, the Veta Pérez sector proved to be the best choice and was selected as the definitive test area, containing 22.10% of the available data. These samples were withheld from the database Figure 2B. By withholding a well-known sector like Veta Pérez, we create a rigorous 'blind test' that proves the model can accurately identify mineralization in areas it has not yet processed. 3.1.3. Training Area The training area comprises 77.89% of the available samples and corresponds to clusters 0, 2, 3, and 4. This data was used to learn from the mineralization phenomenon to be explained, and this learning was then applied to make predictions in the Veta Pérez area, where the information had been previously withheld. 3.1.4. First Stage Evaluation The validation of the different architectures includes both a visual and a numerical component. The visual part aims to identify how well the model can recognize known zones. This is done through a point-by-point comparison between the predicted sample and the actual drill hole sample. The numerical part seeks to identify, through various parameters, which of the applied architecture best responds to the identification of the mineralization phenomenon. The metrics used are precision, F1-score, recall, accuracy, and specificity. Establishing these baseline metrics
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