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Geochemically Constrained Prospectivity Mapping Aided by Unsupervised Cluster Analysis

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Abstract

In this paper, we explore unsupervised cluster analysis to aid mineral prospectivity mapping (MPM) in two aspects: (1) to cluster geochemical data for MPM based on detailed analysis of evidence maps and (2) to explore coherence of spatial signatures at/around mineralized locations as well as outliers of geochemical data. To do so, a systematic procedure is proposed based on the Iterative Self-organizing Data Analysis Techniques Algorithm (ISODATA). Through this procedure, the detailed analysis of evidence maps in Hezuo–Meiwu district, Gansu Province, China, which portray five selected geochemical elements, showed that clusters with and without mineralized locations provide insight to weighing of each evidence. Finally, through the integration of evidence maps, the favorability score map yielded high AUC (> 0.80) for delineating various mineralized locations in the study area, which proves the efficacy of unsupervised cluster analysis as an aid to MPM. Moreover, the coherence of spatial signatures of known mineralized locations, which comprise a training dataset, is vital to data-driven MPM. Groupings of mineralized locations based on the ISODATA and visual inspection supported by PCs from principal component analysis imply that different deposit types may share the same or similar spatial signature and outliers in geochemical data may be potential training samples used for data-driven MPM. Mineralized locations of the same deposit type may show significant dissimilarity. However, this provides insights into selecting mineralized/non-mineralized locations for creation of training datasets. Interestingly, in the study area, major mineralized locations in zones divided by regional fault are clustered separately into two groups. This result not only proves that cluster analysis is effective for exploring the coherence of spatial signatures at/around mineralized locations, but it also justified our previous study, whereby we performed MPM by zones using machine learning algorithms.

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Figure 1

modified from Sui et al. 2017). Inset map of China from: http://bzdt.ch.mnr.gov.cn/

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Acknowledgments

Funding support for this research was derived from the National Key Research and Development Program of China (Project No. 2017YFC0601501), The China National Mineral Resources Assessment Initiative (Project Nos. 1212010733806 and 1212011120140), National Natural Science Foundation of China (NNSFC, Project No. 42002298) and China Scholarship Council (CSC No.201906400022).

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Authors and Affiliations

  1. School of Earth Sciences and Resources, China University of Geosciences Beijing, 29 Xueyuan Road, Beijing, 100083, China

    Shuai Zhang & Yang Xu

  2. School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Westville Campus, Durban, South Africa

    Shuai Zhang & Emmanuel John M. Carranza

  3. MlR Key Laboratory of Metallogeny and Mineral Resource Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing, 100037, China

    Keyan Xiao, Zhenghui Chen, Nan Li, Jie Xiang, Li Sun & Yang Xu

  4. Development and Research Center, China Geological Survey, Beijing, 100037, China

    Hantao Wei

  5. Technical Guidance Center for Mineral Resources Exploration, Ministry of Natural Resources, Beijing, 100120, China

    Hantao Wei

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  1. Shuai Zhang
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Correspondence to Shuai Zhang.

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Zhang, S., Carranza, E.J.M., Xiao, K. et al. Geochemically Constrained Prospectivity Mapping Aided by Unsupervised Cluster Analysis. Nat Resour Res 30, 1955–1975 (2021). https://doi.org/10.1007/s11053-021-09865-x

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