Identifying mineral deposits with minimal environmental impact requires the optimization of heterogeneous datasets. This study validates a rapid geospatial exploration methodology using statistical distances to identify patterns in limited raw data. The approach was applied to a Cu-Ag stratabound deposit in Tiltil, Metropolitan Region, Chile. The method consists of processing diverse spatial variables to generate similarity maps based on user-defined criteria, utilizing a statistical comparison of variable distributions between known mineralized zones, such as El Olivo, Esmeralda, and El Manzano, and unexplored areas. Results demonstrate that the application of statistical distances effectively delineates high-probability mineralization zones, where all 12 generated targets coincided with previously documented mineralized bodies. Specifically, the Total Variation Distance (TVD) yielded the highest precision and contrast for target discrimination. This methodology proves effective for small-scale mining exploration and is potentially adaptable to copper porphyry systems at district and regional scales, significantly optimizing resource allocation in early-stage exploration.
Ojeda-Carreño et al. (Mon,) studied this question.