MINERÍA la mejor puerta de acceso al sector minero EDICIÓN 578 / NOVIEMBRE 2025 88 Method Machine learning models are trained using geological logs at three deposits to test the applications of proxy and pathfinder logging under different geological environments. This protocol derives a general solution with wide applicability for feature engineering of geological logging. Copper Mine – Introduction The mine is located at Region III Chile. The district is characterized by an early-Cretaceous volcanic-sedimentary arc sequence with mineralization hosted primarily in the upper part of the Lower Andesite member Punta del Cobre Formation, which is overlain by volcano-sedimentary and dacite members. This host sequence consists of a thick succession of volcanic andesite flows and intercalated volcanoclastic breccias. This is overlain by the marine-sedimentary Chañarcillo Group. To the west the Copiapó batholith (diorite to quartz monzonite) was emplaced during a period of regional tectonic reversal from extensional to transpressional. Geochronological studies infer that the main phase of mineralization overlaps with the two major early phases of the Copiapó batholith emplacement, although there is no conclusive evidence to indicate from the exposed phases of the batholith that it was the source of mineralizing fluids (del Real, Thompson and Carriedo, 2018). The orebodies are mineralized with magnetite, chalcopyrite, and pyrite, with lesser pyrrhotite and sphalerite as veinlets and disseminations (locally semi-massive sulphide bodies) and is hosted within highly altered favorable lithological units, fault zones and breccias. These mineralizing fault systems are predominantly controlled by a series of high-angle, northwest-striking regional structures. The stratigraphically controlled replacement mineralization forms extensive stratabound ore bodies that are locally termed “Mantos”. Textural studies indicate that the hydrothermal system evolved and progressed outwards and upwards from sub-vertical feeder structures as the replacement occurred. These sub-vertical feeder structures manifest as the mineralized fault zones and breccias, which acted as primary conduits for hydrothermal fluids to access and spread laterally within the more permeable and reactive andesitic host rocks. A distinctive early sodic-calcic alteration (actinolite, albite, scapolite, epidote) characterizes the district, which is locally overprinted by potassic ± calcic alteration (actinolite - biotite (green – high Mg) - K-feldspar) alteration associated with the Manto mineralization (Ichii et al., 2007). This later potassic assemblage is texturally and genetically linked to the main chalcopyrite mineralization event, defining the core of the economic orebodies. Copper Mine – Methodology Copper resource modelling integrated with lithology logging is another example of a proxy logging application. Roughly half the mine drillhole data set is visually deemed to be barren and remains unassayed for copper or any other element. However, irrespective as to whether the core remains unassayed, it cannot be assumed to be barren (~0.0% Cu) from a modelling perspective. Although the underground mine has a relatively high cutoff grade (0.5% Cu), a weakly mineralized 0.2% Cu assay is fundamentally different from barren 0.0% Cu assay, as the former sample may indicate mineralization in close proximity, while the latter is likely to have little significance and be indicative of a barren zone. Two solutions are proposed to resolve the issue: 1.D(Cu, ZFCU) ~ D(Cu), Utilize an independent input channel. Rather than assuming that unassayed core can be assigned a 0.0% Cu value, use the ZFCU (zero filled copper) as an extra channel into the model to indicate material that has been visually logged to be barren but is unassayed. 2.D(Cu, ZFCU) ~ D(Cu, ZFCU). Utilize both as an independent input channel and as a measure of ground truth. In addition to approach 1, for samples that are unassayed and logged as barren, assign 0.0% Cu, and use it to teach the model the correct answer for the copper grade for a certain block. It is impossible to sample the true copper distribution for unassayed core without additional data collection (assaying) and it is improbable the mine will assay significant quantities of core previously logged as barren or weakly mineralized and excluded from their mineral resource model. Three DL models are created: 1) using available Cu assays only, 2) using the Cu assays and unassayed drill core (ZFCU) as an independent input and 3) using the second method as input but with a measure of ground truth for areas that are geologically logged as barren (i.e. unassayed). Own elaboration based on Stratum AI data. Figure 5. Target 6: Historical Drilling vs Drill Plan.
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