Mineral grade estimation using gradient boosting regression trees

ABSTRACT

Resources estimation is one of the critical tasks to evaluate the economic feasibility of a mineral deposit. Traditional prediction workflows, which often involve kriging and inverse distance weighting methods, may not always be suitable to estimate mineral grades for every type of mineralisation. In this study, we present a grade estimation workflow using gradient boosting-based machine learning methods, namely, XGBoost, LightGBM and CatBoost. The case study demonstrated that the three gradient descent-based models performed better than the OK method. XGBoost model demonstrated the best estimation performance with an $R^2$ of 0.728 accuracies, whereas traditional Ordinary Kriging (OK) model yielded 0.651 for $R^2$.

KEYWORDS:

• Machine learning
• XGBoost
• LightGBM
• CatBoost
• ordinary kriging
• grade estimation

Disclosure statement

No author associated with this paper has disclosed any potential or pertinent conflicts which may be perceived to have impending conflict with this work.