MINERÍA la mejor puerta de acceso al sector minero EDICIÓN 578 / NOVIEMBRE 2025 91 Table 2. AI Guided Drillholes Results Own elaboration based on Stratum AI data. Assuming the same number of meters drilled between the two years, the estimated savings per meter for each kT verified is 25%. This study also concludes that efficient drilling to target potential mineralization zones does cause a substantial decrease in drilling costs. Conclusions 1.ML application at the copper mine has demonstrate the efficacy and best practices surrounding the use of proxy logging, the use of visual logging as a proxy for geochemical assays in resource modelling. Visual logging is best used as a supplementary input channel into DL models, including but not limited to binary codes for core unassayed but visually assumed to be barren for Cu modelling. Additionally, for coordinates that do not have geochemical assays but do have logging; logging, despite its limitations, is a beneficial example of ‘ground truth’ by which to each the model correct answers by converting geological logs to their respective most likely geochemical proxy, for example, converting visually barren core to 0.0% Cu. 2.The result of the drilling program was 7.7kT or ~$64M in-situ value of economic copper that was not found by the existing resource model at Atacama Kozan. Furthermore, this study showed that efficient pad placement, spacing, and orientation also reduced drilling costs by 25% over the baseline drilling done by the site between 2020 and 2021. 3.The results have determined that DL models that use geological logs as input are more accurate than DL models based exclusively on geochemistry and/or Kriging models, utilizing categorical indicator Kriging (CIK) and/ or mineralized/unmineralized domaining in a wide range of deposit classes. Both pathfinder and proxy logging proved to be highly applicable in machine learning resource modelling and can be used both to reduce the missed mineralization rate, false positive rate, and mathematically identifying areas where unique economic ore can be found. Furthermore, retaining high grade information that is usually lost in compositing can lead to a natural “downgrade” to the quality of any area of interest within an orebody; for complex deposits like IOCG, high grade information should be retained to find the high-grade pockets within a larger but more marginally mineralized zone. These lithological and compositing considerations done with the inputs directly into the DL model yielded the aforementioned drill program results. Acknowledgements The authors wish to acknowledge the anonymous reviewers who approved the contents and structure of the paper. S.C.M. Atacama Kozan, for permission to access the Atacama Kozan mine database and discussions with Katsuhito Terashima. The authors also acknowledge Ady Aguilar for her help in creating several figures for the paper. Bibliographic references del Real, I, Thompson, J F H and Carriedo, J. 2018. Lithological and structural controls on the genesis of the Candelaria-Punta del Cobre Iron Oxide Copper Gold district, Northern Chile: Ore Geology Reviews 102:106153 First, D. M, Sucholutsky, I, Mogilny D and Yusufali F. 2023. Introducing deep learning and interpreting the patterns – a mineral deposit perspective; in Proceeding Mineral Resource Estimation Conference 2023, pp 2-14, (The Australasian Institute of Mining & Metallurgy). Glacken, I M and Blackney, P C J. 2022. Categorical and multiple indicator Kriging – are we ignoring the geology? In Proceeding 12th International Mining Geology Conference 2022, pp 67-77, (The Australasian Institute of Mining & Metallurgy). Glacken, I, Rondon O and Levell, J. 2023. Drill hole spacing analysis for classification and cost optimization – a critical review of techniques; in Proceeding Mineral Resource Estimation Conference 2023, pp 179-191, (The Australasian Institute of Mining & Metallurgy). Ichii, Y, Abe, A, Ichige, Y, Matsunaga, J, Miyoshi, M, Furuno, M and Yokoi, K. 2007. Copper exploration of the Atacama Kozan Mine, Region III, Chile, Shigen-Chishitsu, 57(1): 1-14 Meyes, R, Lu, M, Waubert de Puiseau, C and Meisen, T. 2019. Ablation studies in artificial neural networks. ArXiv preprint arXiv:1901.08644. Available from: <https:// arxiv.org/pdf/1901.08644.pdf> [Accessed: 12 January 2024]. O'Shea, K and Nash, R. 2015. An introduction to convolutional neural networks, arXiv preprint arXiv:1511.08458. Available from: <https://arxiv.org/pdf/1511.08458.pdf> [Accessed: 12 January 2024]. Sims, D A. 2023. An estimation error; in Proceeding Mineral Resource Estimation Conference 2023, pp 246-249, (The Australasian Institute of Mining & Metallurgy). Zhang, G and Glacken, I. 2023. Best practise in Multiple Indicator Kriging (MIK) – importance of post-processing and comparison with Localised Uniform Conditioning (LUC); in Proceeding Mineral Resource Estimation Conference 2023, pp 76-85, (The Australasian Institute of Mining & Metallurgy).
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