Machine Learning—A Review of Applications in Mineral Resource Estimation

Nelson K. Dumakor-Dupey and Sampurna Arya
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Nelson K. Dumakor-Dupey: Department of Mining and Mineral Engineering, College of Engineering and Mines, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
Sampurna Arya: Department of Mining and Mineral Engineering, College of Engineering and Mines, University of Alaska Fairbanks, Fairbanks, AK 99775, USA

Energies, 2021, vol. 14, issue 14, 1-29

Abstract: Mineral resource estimation involves the determination of the grade and tonnage of a mineral deposit based on its geological characteristics using various estimation methods. Conventional estimation methods, such as geometric and geostatistical techniques, remain the most widely used methods for resource estimation. However, recent advances in computer algorithms have allowed researchers to explore the potential of machine learning techniques in mineral resource estimation. This study presents a comprehensive review of papers that have employed machine learning to estimate mineral resources. The review covers popular machine learning techniques and their implementation and limitations. Papers that performed a comparative analysis of both conventional and machine learning techniques were also considered. The literature shows that the machine learning models can accommodate several geological parameters and effectively approximate complex nonlinear relationships among them, exhibiting superior performance over the conventional techniques.

Keywords: resource estimation; geostatistics; machine learning; kriging; reserve estimation; ore (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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