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Prospectivity Mapping for Porphyry Cu–Mo Mineralization in the Eastern Tianshan, Xinjiang, Northwestern China

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Abstract

In order to comprehensively utilize regional-scale geological, geochemical and geophysical datasets for future exploration of undiscovered porphyry Cu–Mo polymetallic deposits (PCMPDs) in the Chinese Eastern Tianshan orogenic belt, three data-driven mineral prospectivity mapping (MPM) methods, namely ordinary weights of evidence (WofE), fuzzy weights of evidence (FWofE) and logistic regression (LR), were employed to integrate these datasets for mapping prospectivity of undiscovered PCMPDs. Firstly, the geological setting and mineralization of PCMPDs in the Eastern Tianshan district are reviewed. Then, spatial datasets based on geological maps, stream sediment geochemical data, and Bouguer gravity and aeromagnetic data are introduced, and on the basis of the prospecting model for PCMPDs, layers of structural, lithological, geophysical and geochemical evidences are constructed using the spatial datasets by means of GIS-based techniques. Finally, these evidential layers were integrated by using the WofE, FWofE and LR methods to obtain posterior probability maps of PCMPDs and the results are critically compared. The main conclusions are that: (1) the porphyry Cu–Mo mineralization in the Eastern Tianshan was occurred in the subduction boundary of the Late Paleozoic Dananhu-Dacaotan arc system of Kanguertag-Huangshan deep fault belt. This geological inference is supported by all the data-driven MPM methods; (2) the conditional independence assumption for both WofE and FWofE can be easily violated in practical applications. This issue seems very difficult to be circumvented due to geological correlations of evidence layers; (3) the uncertainty of the LR modeling approach particularly with respect to models using multiclass response variables mainly arises from over-fitting of the (ln-transformed) linear relationship; and (4) if there is no need for estimation of the number of undiscovered PCMPDs, the prospectivity map biasedly estimated by either WofE or FWofE modeling can be recommended for targeting new exploration areas with more detailed reconnaissance of potential undiscovered PCMPDs. Otherwise, the prospectivity map unbiasedly estimated by LR modeling with binary evidence modeling approach can be priority of use.

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Acknowledgments

Thanks are due to the guest editors of this special issue (Drs. Mark Mihalasky and Vesa Nykänen), the chief editor of NRR (Dr. John Carranza) and two anonymous reviewers for their constructive comments and suggestions. The main idea of this paper has been presented on IAMG2018, September 2–8, 2018, Olomouc, Czech Republic. This research was financially supported by the National Natural Science Foundation of China (Nos. 41872245, 41502310), the Fundamental Research Funds for the Central Universities (No. 17lgpy48) and the Natural Science Foundation of Guangdong Province, China (No. 2015A030310246).

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  1. School of Earth Sciences and Engineering, Sun Yat-sen University, Guangzhou, 510275, China

    Fan Xiao, Kaiqi Wang, Weisheng Hou, Zhenghai Wang & Yongzhang Zhou

  2. Guangdong Provincial Key Laboratory of Geological Process and Mineral Resource Exploration, Guangzhou, 510275, China

    Fan Xiao, Weisheng Hou, Zhenghai Wang & Yongzhang Zhou

  3. Guangdong Provincial Key Lab of Geodynamics and Geohazards, Guangzhou, 510275, China

    Fan Xiao, Weisheng Hou, Zhenghai Wang & Yongzhang Zhou

  4. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China

    Fan Xiao, Weisheng Hou, Zhenghai Wang & Yongzhang Zhou

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Xiao, F., Wang, K., Hou, W. et al. Prospectivity Mapping for Porphyry Cu–Mo Mineralization in the Eastern Tianshan, Xinjiang, Northwestern China. Nat Resour Res 29, 89–113 (2020). https://doi.org/10.1007/s11053-019-09486-5

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