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3D Au Targeting using Machine Learning with Different Sample Combination and Return-Risk Analysis in the Sanshandao-Cangshang District, Shandong Province, China

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Abstract

Three-dimensional (3D) mineral prospectivity mapping (MPM) uses mathematical models to integrate different types of 3D data related to mineralization to obtain mineral prospectivity information in 3D space. Existing geological data contain known deposits, non-deposits and unknown ore-bearing data, corresponding to positive samples, negative samples and unlabeled samples respectively in MPM. Different sample combination types require different mathematical models. In this paper, support vector machine class (SVMC) machine learning method is selected to compare the influence of different sample combination types on prediction results. The SVMC is a one-class SVM (OCSVM) model based on positive-only samples, the SVM is based on both positive and negative samples, and the bagging-based positive-unlabeled learning algorithm with SVM base learner (BPUL-SVM) is based on both positive and unlabeled samples. The study area is in the Sanshandao-Cangshang offshore and onshore Au district, where there are Sanshandao, Cangshang and Xinli large- and super-large-scale Au deposits. Moreover, the discovery of large-scale Sea Au deposits in the sea area indicates the great potential for mineralization in the district. According to the metallogenic geological characteristics, the Au deposits in the Sanshandao-Cangshang district are controlled by the NE-striking fault and are closely related to the Linglong intrusions and Guojialing intrusions. The ore-bearing intrusion shows low density and low-moderate magnetic susceptibility. Because the Au orebodies hosted in the Sanshandao fault and its secondary faults, the NE-striking faults are key to delineating the targets. In this paper, weights of evidence (WofE), OCSVM, SVM and BPUL-SVM are used to MPM, and the prediction-area (P-A) plot method is used to delineate the targets. According to the ROC curve, F1 score and P-A plot evaluation methods, the model performance from high to low is BPUL-SVM13, SVM12, WofE and OCSVM. The BPUL-SVM model performance with samples combination types of positive samples and unlabeled samples was optimum in SVMC prediction models. The Markov chain Monte Carlo (MCMC) simulation and return-risk evaluation model are used to evaluate the return and risk of the targets and finally determine the I-level targets with high return and low risk. The delineated targets are mainly distributed along the F2 and F3 faults (Sanshandao-Cangshang fault). Combined with the mineralization regularity, the deep and periphery space of the known deposits are important to explore Au orebodies. The delineated targets are important to explore offshore and onshore Au orebodies in the Sanshandao-Cangshang district.

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Acknowledgements

The authors gratefully acknowledge two anonymous reviewers for their useful comments to improve this paper. Funding support for research was provided by the “Deep-time Digital Earth” Science and Technology Leading Talents Team Funds for the Central Universities for the Frontiers Science Center for Deep-time Digital Earth, China University of Geosciences (Beijing) (Fundamental Research Funds for the Central Universities; grant number: 2652023001), the National Key Research and Development Programs of China (Grant No. 2022YFC2903604) and 2021 Graduate Innovation Fund Project of China University of Geosciences, Beijing (Grant No. ZD2021YC008). The authors thank Ruixi Li, Jing Li, Nini Mou, Shuren Yang, Xiaoning Liu, Leilei Huang and other group members for their help in this study. The authors thank all those who provide help to this article.

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Authors and Affiliations

  1. Frontiers Science Center for Deep-time Digital Earth, China University of Geosciences (Beijing), Beijing, 100083, China

    Meng Gao, Gongwen Wang & Siyan Qi

  2. School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing, 100083, China

    Meng Gao, Gongwen Wang & Siyan Qi

  3. Beijing Key Laboratory of Land and Resources Information Research and Development, Beijing, 100083, China

    Gongwen Wang

  4. Department of Geology, University of the Free State, Bloemfontein, South Africa

    Emmanuel John M. Carranza

  5. Shandong Institute of Geological Survey, Jinan, 250013, China

    Wen Zhang & Xiuzhang Li

  6. Development Research Center of China Geological Survey, Beijing, 100037, China

    Zhenshan Pang

  7. Shandong Gold Group, Jinan, 250100, China

    Fengli Xiao

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Gao, M., Wang, G., Carranza, E.J.M. et al. 3D Au Targeting using Machine Learning with Different Sample Combination and Return-Risk Analysis in the Sanshandao-Cangshang District, Shandong Province, China. Nat Resour Res 33, 51–74 (2024). https://doi.org/10.1007/s11053-023-10279-0

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  • DOI: https://doi.org/10.1007/s11053-023-10279-0

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