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Machine Learning of Mineralization-Related Geochemical Anomalies: A Review of Potential Methods

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Abstract

Research on processing geochemical data and identifying geochemical anomalies has made important progress in recent decades. Fractal/multi-fractal models, compositional data analysis, and machine learning (ML) are three widely used techniques in the field of geochemical data processing. In recent years, ML has been applied to model the complex and unknown multivariate geochemical distribution and extract meaningful elemental associations related to mineralization or environmental pollution. It is expected that ML will have a more significant role in geochemical mapping with the development of big data science and artificial intelligence in the near future. In this study, state-of-the-art applications of ML in identifying geochemical anomalies were reviewed, and the advantages and disadvantages of ML for geochemical prospecting were investigated. More applications are needed to demonstrate the advantage of ML in solving complex problems in the geosciences.

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Acknowledgments

The author thanks Dr. John Carranza and two anonymous reviewers for their edits and comments that improved the manuscript. The author also thanks Yihui Xiong from the China University of Geosciences for preparing the figure and checking the references. This research benefited from the joint financial support from the National Natural Science Foundation of China (No. 41522206) and MOST Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences (MSFGPMR03-3).

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  1. State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074, China

    Renguang Zuo

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Zuo, R. Machine Learning of Mineralization-Related Geochemical Anomalies: A Review of Potential Methods. Nat Resour Res 26, 457–464 (2017). https://doi.org/10.1007/s11053-017-9345-4

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