A new approach to dilution prediction of underground mine gold using computing techniques.

Abstract

Controlling ore dilution in underground mining is challenging. In this study, data from a Brazilian gold mine were analyzed, covering 70 chambers and 26 variables. Six key variables were identified through decision tree analysis, forming the basis of a predictive model using advanced soft computing techniques. The constructed Random Forest model (RF-A) significantly outperformed two predictive equations from the literature, achieving an R² of 0.9161 compared to 0.3009 and 0.1597 from the literary equations. Validation of RF-A with random subsampling resulted in a marginal decrease in the R² value to 0.3060, suggesting a nonlinear correlation between mining variables and dilution, highlighting the inadequacy of linear analysis methods. By dividing the dataset into three subsets representing different mineral bodies, three new Random Forest models (RF-CV, RF-CB, and RF-LJ) were created, with R² values of 0.5465, 0.5295, and 0.4525, respectively. These results underscore the need to tailor models to specific geological contexts and demonstrate the potential of machine learning techniques in predicting dilution in complex underground mining scenarios.