Hybrid geostatistical and machine learning for gold grade estimation

Abstract

The evaluation of ore grade is a basic part of the process of mine planning, production scheduling and resource evaluation. The nugget effect and irregular mineralisation that occurs in some greenstone belt deposits on the Zimbabwean Archean causes problems in estimating accurately. The traditional geostatistical models like Ordinary Kriging (OK) produce smoothed estimates which have systematic underestimation in high-grade areas and overestimation in low-grade areas, thus affecting the deposit selectivity. In addition, machine learning (ML) techniques can capture the more complex nonlinear grade relationships, but they are unable to model spatial continuity and can be unrealistic in predicting grades. The hybrid OK-Gradient Boosting (GB) model was designed and evaluated with the drill-hole data of a gold mine in Zimbabwe. The spatial baseline was produced using OK and GB was trained to correct the systematic residual errors. The hybrid model was found to have the maximum overall explanatory power (R²=0.8795), competitive prediction accuracy and increased spatial realism. A spatial prediction map and extraction priority zone classification was created to aid operational mine planning.