MINERÍA la mejor puerta de acceso al sector minero EDICIÓN 578 / NOVIEMBRE 2025 85 The ML algorithms used in this paper are written in Python, a programming language that distinguishes itself from other programming languages with its flexibility, simplicity and large number of available open-source tools required to create modern software, including machine learning algorithms. Python helps software engineers focus on solving logical problems rather than spending time on the basics of the programming language. This is one of the primary reasons that Python is the language of choice for machine learning and data science in general. PyTorch is the ML library that houses the open-source tools used to construct neural network layers. These neural network layers are paired with CUDA (Compute Unified Device Architecture), a computing platform developed by NVIDIA to interact with Graphics Processing Units (GPUs). NVIDIA is a technology company that designs and manufactures GPUs. Deep Learning Limitations While powerful, DL models are not without limitations. A DL model is inherently error-resistant to a certain level of noise within data but are not totally immune. Unfortunately, most geological logging data is noise from the perspective of its usefulness in resource estimation. Most geological logs carry limited value for copper grade modelling. However, a recently developed method, ablation analysis, has been found to be invaluable when selecting which geological data channels are productive inputs into DL models (Meyes et al., 2019). The ablation analysis method individually runs all potential input channels to identify which ones add value to an estimation. For example, if the objective is to build a gold resource model that is enhanced using geological logging, ablation analysis will produce recommendations akin to D(Au, X) ~ D, where every X is a unique geological logging code, whether it be a lithology, alteration or geotechnical code. However, many particularly larger deposits, have a penchant for numerous unique lithological and alteration logging codes, making the analytical process cumbersome and computationally impractical. The techniques to identify which input channels to use and the most efficient way to encode them, collectively feature engineering, is explained in Methods. Geological Logging Limitations Geological logging has two major applications when being applied to spatial modelling at mine sites: 1) a pathfinder for mine site exploration and resource estimation and 2) as a proxy for a geochemical assay or mineralogical test. Its usefulness as a pathfinder in the discovery of new and/ or missed ore is evident when drill core is logged as being barren or poorly mineralized but contains a geological logging code that is directly or indirectly indicative of high-grade mineralized zones nearby. A common example discussed below is mineralized zone (MZ) logging. Geological logs can be used as proxies for assays, alteration mineralogy, rock competency, etc. Most mines only prescribe a full assay suite (ICP-MS), detailed mineralogy/ petrology or rock strength tests on a select few mine site samples (as it would be prohibitively expensive to collect thorough assay suites for all inputs used in a resource model). Mines, particularly underground operations, have significant budgetary constraints on data collection expenditure, therefore the number of samples assayed, especially from third party laboratories, are restricted and/or relegated to mine site laboratory, apart from a few confirmation assays. The total meterage of core assayed is also restricted, often resulting in weak to moderately mineralized core not being assayed at all if it visually appears to not host economic mineralization, thereby directly impacting modelling accuracy. Own elaboration based on Stratum AI data. Figure 3a. False positive mineralization (>0.5% Cu) 2021 – 2022 for the copper mine. Own elaboration based on Stratum AI data. Figure 3b. Missed mineralization (>0.5% Cu) 2021 – 2022 for the copper mine.
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