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Machine Learning—A Review of Applications in Mineral Resource Estimation

Author

Listed:
  • 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)

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.

Suggested Citation

  • Nelson K. Dumakor-Dupey & Sampurna Arya, 2021. "Machine Learning—A Review of Applications in Mineral Resource Estimation," Energies, MDPI, vol. 14(14), pages 1-29, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4079-:d:589582
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    References listed on IDEAS

    as
    1. J. Taboada & Á. Saavedra & C. Iglesias & E. Giráldez, 2013. "Estimating Quartz Reserves Using Compositional Kriging," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-6, May.
    2. Bert Van Wee & David Banister, 2016. "How to Write a Literature Review Paper?," Transport Reviews, Taylor & Francis Journals, vol. 36(2), pages 278-288, March.
    3. Phillip Crowson, 2011. "Mineral reserves and future minerals availability," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 24(1), pages 1-6, July.
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    1. Meihui Yang & Siqi Zhang & Haochong Huang & Yuanyuan Ma & Sibo Hao & Zili Zhang & Zhiyuan Zheng, 2022. "Insights into a Mineral Resource Chlorite Mica Carbonate Schist by Terahertz Spectroscopy Technology," Energies, MDPI, vol. 15(17), pages 1-10, August.

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