Figure 1A.- Analysis of rock types and structural orientations showing favorable correlation with high-grade silver mineralization. 1B.- Orientation of structures favorable for mineralization, given their occurrence in the vicinity as the silver grade increases. geophysics to establish a framework that enhances target identification in rugged terrain. 2. AVAILABLE INFORMATION A total of 45 variables were integrated. The dependent variable is silver (Ag) concentration with a threshold of 50 ppm Ag. 2.1 Dependent Variable The dependent variable defined for this study is silver (Ag) concentration, sourced from the geochemical database of all available drill holes. A threshold of 50 ppm Ag was established as the cut-off grade for the predictive exercise. This specific concentration represents the minimum economic grade acceptable for the processing plant and was defined as the primary target by the mine’s exploration team 2.2 Exploratory Variable The predictive or explanatory variables integrated into the model include lithology (both surface and subsurface), hydrothermal alteration, structural data, and geophysics (magnetometry and radiometry). The effective projection area is constrained by the overlap of these datasets; in this instance, the combined explanatory variables cover a surface of 109 km² out of the total 248 km² of the district. 2.2.1 Lithology Comprises 17 surface and eight subsurface units. Units such as ‘kpa’ and ‘tcr’ show favorable correlation with mineralization Figure 1A. 2.2.2 Alteration Eight classes derived from ASTER satellite imagery, including argillic, phyllic, and silica categories. 2.2.3 Structures Categorized by strike orientation. The district features five structural blocks where vein orientations shift from NE in the west to NS in the east Figure 1B. 1A 1B
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