A REVIEW OF MACHINE LEARNING ALGORITHMS TO MINERAL EXPLORATION AND MAPPING
Abstract
The current breakthroughs in smart mining offerings have brought a new wave of real-time data production and analyses, and the mining sector has leaped forward embracing machine learning (ML) to streamline their activities, enhance safety, and increase sustainability. This review examines 87 new publications, and a careful study of 42 significant papers to examine how ML is being used in several mining disciplines, including mineral exploration, ore grade modeling, process optimization, and environment management. The results point out the fact that the ML research is highly focused on the application of surface mining where numerous challenges and opportunities are built on complexity and abundance of data. Such techniques as deep neural networks (DNNs) and support vector machines (SVMs) are popular because they show good results in predictive maintenance, ore classification, and yield optimization, but techniques such as ensemble methods and reinforcement learning are becoming increasingly popular because they are more adaptable. Though the classic criteria of evaluation such as the robustness of regression tend to be widespread, more sophisticated tools such as the cross-validation and confusion matrices are on the rise. Data heterogeneity, model transparency, and data incorporation of real-time sensor data continue to remain a problem. The upcoming studies are recommended to focus on hybrid solutions that combine ML with physics-based models, exploit edge computing to get on-the-fly realizations, and resolve the ethical aspect of AI automation. All in all, the review highlights the revolutionary properties of ML in the mining field and the necessity of a more coordinated work of data scientists, engineers, and stakeholders to facilitate the development of efficient, smart, and sustainable mining trends.
Keywords -(Smart mining, machine learning, deep neural networks, support vector machines, predictive modeling, mineral extraction, real-time data analytics).
