Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

TLDR

Smart mining technology now generates large real‑time data streams, prompting active machine‑learning research in the industry. This review examined 109 papers from the past decade on ML for mineral exploration, exploitation, and reclamation. The authors systematically analyzed research trends, ML models, and evaluation methods across the papers, noting that models were mainly assessed by RMSE and R². ML research has increased since 2018, mainly for exploration, with support vector machines leading, followed by deep learning.

Abstract

Recent developments in smart mining technology have enabled the production, collection, and sharing of a large amount of data in real time. Therefore, research employing machine learning (ML) that utilizes these data is being actively conducted in the mining industry. In this study, we reviewed 109 research papers, published over the past decade, that discuss ML techniques for mineral exploration, exploitation, and mine reclamation. Research trends, ML models, and evaluation methods primarily discussed in the 109 papers were systematically analyzed. The results demonstrated that ML studies have been actively conducted in the mining industry since 2018, mostly for mineral exploration. Among the ML models, support vector machine was utilized the most, followed by deep learning models. The ML models were evaluated mostly in terms of their root mean square error and coefficient of determination.