MINERÍA la mejor puerta de acceso al sector minero EDICIÓN 578 / NOVIEMBRE 2025 84 Keywords: High-Confidence Expansion Drilling, AI, Geological Logging Proxies. Objectives 1.To create a method of reliably feature engineering geological logging for mine-site spatial modelling. Feature engineering refers to the selection criteria and techniques needed to incorporate data into machine learning. 2.To evaluate whether DL models that use geological logging are more accurate than models based entirely on geochemistry and/or Kriging models (based on categorical indicator Kriging (CIK) and/or mineralized zone (MZ) domaining). 3.To create a computationally efficient method for screening which geological logs are useful for DL modelling prior to model training. The deposit where the pathfinder screening algorithm (PSA) system is applied is a Copper mine, Manto-type iron oxide copper gold deposit, located in the Candelaria-Punta del Cobre district, Region III, Chile. Background Machine Learning and Deep Learning Recently, machine learning (ML) has emerged as a powerful tool for revealing complex patterns in data. At its core, ML algorithms learn from historical data to better forecast a future pattern or trend. Although ML, and more generally, AI is defined as such above, DL is the term used for one of the most powerful ML algorithms; it uses multiple layers of artificial neurons that are composited into a deep neural network (i.e. convolutional neural network (CNN) (O'Shea and Nash, 2015). The concept is loosely modelled on the way neuroscientists believe the brain behaves when it identifies patterns in very large data sets. DL has seen much success in the field of image recognition (e.g. medical imaging) as well as machine translation, the AI process of automatically translating text from one language to another (Goodfellow, Bengio and Courville, 2016). ML and DL’s inherent advantage over traditional Kriging methodologies is its unique ability to leverage non-linear correlation trends, its capacity to model geological logging data and identify high quality data sources by learning from historical data. Geological logging data can be defined as any type of data that is collected through visual inspection of samples, done typically with drill core. While geological logs are inherently qualitative in nature when compared with assays, it nevertheless has significant value as a dataset. The biggest advantage of geological logging is its cost-efficient acquisition. However, the major challenges of working with geological logs in spatial modelling include identifying how to best leverage the data while considering its qualitative limitations, interpretation bias (e.g. a deposit logged by multiple geologists with evolving interpretations) and a relatively low SNR ratio (given that some of the parameters logged may be irrelevant to spatial modelling). The DL algorithm and nomenclature referenced in this paper by First et al (2023) is repeated in this paper. For example, DRC(Au, MZ) ~ DR references a gold resource model that uses diamond drillholes (D), RC drillholes (R), rock-chip (C) assays as inputs into the model, Au assays and MZ geological logging data as a separate input channels and DR (on the righthand side) as drillholes (D) and RC drillholes (R) as a proxy for ground truth or the ‘correct’ answer, by which the DL model learns the spatial distribution of gold. The primary advantage of subdividing the inputs from the ground-truth readings is to manage lower quality assays, such as rock-chip samples, which may be useful in spatial modelling but cannot be relied on greatly to teach the model the accurate answer on a block level. In other words, rock-chipping 10g/t Au from a block may be useful information for a machine learning model to understand the spatial distribution of gold; however, that sample cannot be used to denote the entire 3m x 3m x 3m smallest mining unit (SMU) as 10g/t Au given that the chip samples could be collected inside of a 50cm vein, a highly bias form of data. Atacama Kozan, “Introduction presentation,” PowerPoint presentation, 2021. Figure 1. S.C.M Atacama Kozan Geological Profile. Atacama Kozan, “Introduction presentation,” PowerPoint presentation, 2021. Figure 2. Visual inspection of low-grade material from the Copper Mine.
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