Enhancing ore grade estimation with artificial intelligence

Introduction Accurate prediction of mineral grades is a fundamental step in mineral exploration and resource estimation, which plays a significant role in the economic evaluation of mining projects. Currently available methods are based either on geometrical approaches or geostatistical techniques that often considers the grade as a regionalised variable (Kaplan & Topal, 2020). Geostatistics has been widely used for qualitative estimation of ore deposits for many decades. However, ore quality does not vary uniformly in three dimensions which results in a poor quality estimation with the conventional geostatistical methods (Jain et al., 2022). Consequently, these limitations and complexities inspired researchers to investigate alternative approaches that can be utilised to overcome such obstacles. Over the past few decades, several researchers focused on various computational learning techniques that can predict grades more accurately without having to rely on an unde...