MINERÍA la mejor puerta de acceso al sector minero EDICIÓN 578 / NOVIEMBRE 2025 86 However, fortuitously geological logging can serve as a proxy for a geochemical assay and alteration mineralogy. While out of scope for this paper, geotechnical parameters such as RQD, have the potential to be a proxy for rock competency that cannot easily be measured in a laboratory. Geological logging is already used in spatial modelling in the mining industry in two major ways: categorical indicator Kriging (CIK) and MZ domaining (Glacken and Blackney, 2022). In categorical indicator Kriging, categorical logs are used to encode whether a sample is oxide, transitional or sulphide and is represented by integer values when applying Kriging to the data; for example, oxide ore is represented by 0, transitional ore by 0.5, sulphide ore by 1. The final estimates are rounded based on the mine’s error tolerance in each class. Some mines may vary the transition ore estimate (e.g. 0.7 considered to be sulphide) depending on processing constraints. Domaining with geological logs has some unique challenges, particularly with respect to nuggety and structurally controlled deposits. Many orogenic and intrusion-related gold deposits, geologically log MZ and quartz veins. Essentially, if the core from the geologist’s perspective visually looks like it is potentially mineralized, it is logged as such, irrespective of the gold assay collected later. An issue of note is that MZ is often used as a descriptor for many of the pre-mineral lithologies, resulting in extra lithological codes. The logging data used to assist in the construction of domains by constraining the mineralized zones of these nuggety deposits, thereby critical to the mineral resource estimation process as there is extreme variation in gold distribution within a small volume. Frequently within these orebodies, unrepresentative (barren or subeconomic) rock-chip or drill core assays can be sampled in very close proximity to well-mineralized drill core; as such, RC chip or rock-chip samples make grade estimation of ore blocks very challenging. While geologists are proficient at handling the non-linear nature of geological logging, there is substantial risk, as the resource model becomes beholden to the subjective interpretation of the geological logging team and potentially an overreliance on categorical indicator Kriging and/ or mineralized zone domaining (Glacken, Rondo and Levett, 2023; Sims, 2023). While these two methods demonstrate that geological logging has inherent value in spatial estimation, they are limited in its usefulness due to Kriging’s inherent linear interpolation-based algorithm. The two methods are also incapable of accurately modelling mixed data types, like unassayed core where it could be interpreted as either barren or weakly mineralized even though it visually appears barren. There are non-linear geostatistical methods that have been applied, like multiple indicator Kriging and localized uniform conditioning; however, they have proved challenging to implement (Zhang & Glacken, 2023). As discussed above, geological logs are critical when manually domaining an orebody but have proved imperfect when modelling due to the subjective nature in the logging process. There is a natural tendency for geologists to subdivide or split the lithologies instead of looking holistically for commonality within the data such that productive modelling inputs are derived by lumping lithologies together. This results in situations where a large component of the ‘signal’ is lost such that the detailed information is not incorporated into the Kriging model. To circumvent this issue, many mines create ever smaller domains with the aim of capturing the geological complexities of the deposit. This results perversely in the domains guiding mine planning and mine site exploration, rather than Kriging estimation. A naïve solution would be to undertake an ablation analysis on the ten most common logging codes. Regrettably, the most common codes are not necessarily the most useful ones for resource modelling as the economic mineral resources is invariably restricted to anomalous geologically zones. Therefore, it has become necessary to derive a method that can screen geological logs with relatively high degree of accuracy for their usefulness in spatially estimating a parameter (e.g. gold or copper value). Own elaboration based on Stratum AI data. Figure 4. AI Unique Mineralization Zones.
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