MINERÍA la mejor puerta de acceso al sector minero EDICIÓN 578 / NOVIEMBRE 2025 83 By: Farzi Yusufali, CEO; David M First, Chief Geologist, and Daniel Mogilny, CTO, Stratum AI. Abstract This study explores the application of geological logging as proxies to economic mineralization using machine learning techniques and evaluation by producing high-confidence expansion drill program targets. The authors introduce chosen features considered and included in the resource modelling process, the relative improvement in block-level estimation metrics compared to the mine site’s resource model, and the drilling results from the mineralization zones identified by this approach. The authors present the results of this study and drill program for an iron-oxide copper group deposit in Candelaria-Punta del Cobre region, Atacama Desert in the northern region of Chile. The author’s demonstrated methods include 1) identifying statistically significant non-linear correlated lithological features using data analysis and visual inspection 2) limited feature selection to prevent overfitting, and 3) evaluation criteria to determine efficacy of the AI-based resource estimation method. The AI-based model’s blind reconciliation of data tested over 8 quarters, with 0.5% Cu cut-off grade, shows an increase of 1.67x reconciled mineralization while maintaining the same sensitivity (false positive rate) as the benchmarked site resource model. The AI-based model includes input channels of assayed Cu and visually logged barren rock where the visually logged barren rock is pattern-matched against assayed 0.0% Cu to teach reduce the error of the visual logs. The model was used to produce a 2000 m underground expansion drilling program; the program successfully identified three new mineralization zones of economic Cu where waste was previously predicted by the benchmark site model. A total of 11 holes were drilled where all holes intersected high grade Cu zones (3 times higher than cut-off), all at least 10m away from known mineralization. The total in-situ value uniquely identified and verified to Measured was 7.7kT of Cu above cut-off grade. Future work should focus on expanding geochemical and lithological datasets and exploring additional geological variables to improve predictive capabilities specific to each deposit and regional archetype. Trabajo con Mención Honrosa en el Foro TIS de PERUMIN 37.
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