MINERÍA la mejor puerta de acceso al sector minero EDICIÓN 578 / NOVIEMBRE 2025 90 conciled mineralization while having the equivalent sensitivity (i.e. false positive rate) as Kriging. D(Cu, ZFCU) ~ D(Cu, ZFCU) uses assayed copper and visually inspected (i.e. barren vs not-barren) copper as two separate channels into the model. The model inputs use unassayed visually barren core as an example of ground truth to teach the model that visually determined barren material has a copper grade of 0.0%. The D(Cu, ZFCU) ~ D(Cu, ZFCU) model has a lower missed mineralization rate than both D(Cu) ~ D(Cu) model (which ignores all visually inspected core) and the D(Cu, ZFCU) ~ D(Cu) model (which uses logged core as an input without ground truth to verify the accuracy of the log). Drill Program Field Testing Extensive testing has been undertaken at Atacama Kozan mine site. The DL models were used to guide a successful 2,000 m underground drilling program that successfully identified three new zones of additional economic copper ore, where waste was previously predicted by the Kriging model. The constraints in which areas are identified as economic and uneconomic as well as the minimum economic volume to be considered worthwhile for adding to mine plan were also considered. Three zones were considered for drilling where the optimal outcome would be to classify as many blocks in those zones as Measured, thereby making it eligible for addition to mine plan. The threshold chosen to classify an area as unique was a 60% difference in contained lbs. Cu where the DL models predict a given volume as economic (above cutoff grade) whereas the site’s Kriging-based model predicts it as waste. Three zones were chosen, mineralization zones 6, 16, and 65 to evaluate the performance of the DL model over Kriging in finding areas of unique economic ore. The eligible mineralization zones are at least 10m away from any known mineralization (denoted in the depletion model or the site’s Kriging model); this would rule out natural extensions from pre-existing mineralization zones. The threshold for success was 0.24kT of in-situ Cu that would be added to mine plan (i.e. verified to Measured). A total of 12 holes were drilled in each of abovementioned clusters to execute this evaluation; the length of the drillholes ranged from 100m to 296m and is drilled from within the infrastructure. Table 1 presents, for each target mineralized zone, the statistics that illustrate the estimated values prior to drilling and the results obtained after drilling Visually, all holes hit high grade intercepts (>0.5% Cu) and each contributed to adding a large percentage of the predicted mineralized target to Measured. Target 65 was interesting as it was predicted outside of a fault zone that was initially understood to have caused mineralization discontinuity. The verification of high-grade mineralization in this zone resulted in re-evaluating that region west of the core infrastructure as being more likely to be a largely justified shear as opposed to a hard fault that would have stopped fluid from traversing that structural discontinuity. Furthermore, it was verified that the orientation and pattern done in previous drilling and marginal Cu grades from those assays were contributing factors to the difference in evaluation of each target prior to the use of the DL models. Furthermore, it shows that certain areas within the overall volume of each target have more high-grade mineralization and those areas should be added to the mine plan first, after which low-cost RC drilling can be employed later to evaluate the remaining lower grade blocks of the volume (after the stopes have been planned). Therefore, the DL model is particularly useful in finding the outlier high grade Cu blocks within a larger volume that would be ordinarily smoothed by a Kriging-based model. The Figures 5, 6 and 7 illustrate the drilling done prior to the evaluation above and the drill plan that was created for each target. These figures emphasize the importance of orientation and retaining high grade assay information when modelling the resource. For example, in target 16, the lone drillhole that hit the boundary did show high grade intercepts, however, the fan pattern used and the length of the holes resulted in the majority of the volume being missed. Targets 6 and 65 historical drilling have suboptimal orientations that cut perpendicular to the high grade mineralization; therefore, when compositing, the high grade is smoothed such that the overall value of these zones are smoothed to marginal or uneconomic volumes. The final result of the drill program added 7.7kT of in-situ copper to the mine plan where two of the verified economic zones were found within infrastructure and other explored into areas outside the infrastructure for expansion. The total number of meters drilled was 2200 meters. Table 2 shows the performance of the DL models as applied to this drill program compared to benchmark drilling done in the previous 2 years. Table 1. AI Unique Mineralization Zones Results Own elaboration based on Stratum AI data.
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