Estimation of Fe Grade at an Ore Deposit Using Extreme Gradient Boosting Trees (XGBoost)

Abstract

Estimating the spatial distribution of ore grade is one of the most critical and important steps to continue investment decision on the deposit. Kriging is the most widely used method to estimate the ore grade while alternative techniques are being developed. Machine learning algorithms can be used as alternative methods to classical kriging. In this paper, Fe grade of a deposit is estimated with XGBoost algorithm, and results are compared with kriging estimation results. For estimation processes, samples collected from the drillholes are used. To mitigate the effect of varying sampling length, both estimations use composites of these samples. Due to the different nature of the estimation methods, different steps have been taken to perform estimations. Results show that XGBoost estimates produced higher ranged estimates which is a desired result in ore grade estimation while minimum and maximum of the estimates were lower and higher than the kriging estimates, respectively. However, like kriging estimates, estimation results were smoother than composites while variance of the XGBoost estimates were lower than variance of composites. This means that even though estimation with XGBoost mitigates the smoothing effect, estimation results suffer from smoothing effect like kriging.