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Resource prediction and assessment based on 3D/4D big data modeling and deep integration in key ore districts of North China

Abstract

The North China district has been subjected to significant research with regard to the ore-forming dynamics, processes, and quantitative forecasting of gold deposits; it accounts for the highest number of gold reserves and annual products in China. Based on the top-level design of geoscience theory and the method adopted by the National Key R & D Project (deep process and metallogenic mechanism of North China Craton (NCC) metallogenic system), this paper systematically collects and constructs the geoscience data (district, camp, and deposit scales) in four key gold districts of North China (Jiaojia-Sanshandao, Southern Zhaoping, Wulong, and Qingchengzi). The settings associated with the geological dynamics of gold deposits were quantitatively and synthetically analyzed, namely: NCC destruction, metallogenic events, genetic models, and exploration models. Three-dimensional (3D) and four-dimensional (4D) geological modeling was performed using the big data on the districts, while the district-scale 3D exploration criteria were integrated to construct a quantitative exploration model. Among them, FLAC3D modelling and the GeoCube software (version 3.0) were used to implement the numerical simulation of the 3D geological models and the constraints of the fluid saturation parameters of the Jiaojia fault to reconstruct the 4D fault structure models of the Jiaojia fault (with a depth of 5000 m). Using GeoCube3.0, multiple integration modules (general weights of evidence (WofE), Boost WofE, Fuzzy WofE, Logistic Regression, Information Entropy, and Random Forest) and exploration criteria were integrated, while the C-V fractal classification of A, B and C targets in four districts was carried out. The research results are summarized in the following four areas: (1) Four gold districts in the study area have more than three targets (the depth is 3000 m), and the class A, B and C targets exhibit a good spatial correlation with gold bodies that are controlled by mining engineering at depths greater than 1000 m. (2) The Boost WofE method was used to identify the target optimization in 3D spaces (at depths of 3000–5000 m) of the Jiaojia-Sanshandao, Southern Zhaoping, and Wulong districts. (3) The general WofE method is based on the Bayesian theory in 3D space and provides robust integration and target optimization that are suitable for the Jiaojia-Sanshandao and Southern Zhaoping districts in the Jiaodong area; it can also be applied to the Wulong district in the Liaodong area using a quantitative genetic model and an exploration model. Random forest is a multi-objective integration and target optimization method for 3D spaces, and it is suitable for the complex exploration model in the Qingchengzi district of the Liaodong area. The genetic model and exploration criteria associated with the exploration model of the Qingchengzi district were constrained by the common characteristics of the gold fault structure, magmatic rock emplacement in North China, and the strata fold and interlayer detachment structure. (4) Based on the gold reserves and the 3D block unit model of the Sanshandao gold deposit in the Jiaojia-Sanshandao district, the gold contents of the 3D block units in class A and B targets of the ore concentration were estimated to be 65.5% and 25.1%, respectively. The total Au resources of the optimized targets below a depth of 3000 m were 3908 t (including 1700 t reserves), and the total Au resources of the targets at depths from 3000 to 5000 m were 936 t. The study shows that the deep gold deposits in the four gold districts of North China exhibit a strong “transport-deposition” spatial correlation with potential targets. These “transport-deposition” spatial models represent the tectonic-magmatic-hydrothermal activities of the metallogenic system associated with the NCC destruction events and indicate the Au enrichment zones.

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Acknowledgements

In terms of scientific computing, Prof. Clayton Deutsch/Academician and Dr. Qu Jianan of University of Alberta in Canada, Prof. Lizhen Cheng of University of Quebec, Prof. Emmanuel John M. Carranza of University of KwaZulu-Natal in South Africa, and Prof. Shuhad D. Khan and Dr. Lei Sun gave support and help; in the exchange of research results, Prof. Jinhui Yang and Prof. Kezhang Qin from the Institute of Geology and Geophysics of the Chinese Academy of Sciences, Profs. Renyi Chen, Keyan Xiao, Zhengle Chen and Jiayong Yan from the Chinese Academy of Geological Sciences, Prof. Yingxi Xue from the Chinese Geological Survey, Prof. Wang Zongqi and Prof. Cheng Zhizhong Senior Engineer, Jianling Xue Senior Engineer, etc. gave highly affirmation and perfect suggestions; in the aspect of data collection and field investigation, we got the support of Deputy Chief Geologist Fengli Xiao of Shandong Gold Group, Chief Engineer Rifu Liu and Manager Yanbo Xu of Shandong Gold Geology and Mineral Exploration Co., Ltd., Deputy Director Zhaofa Song and Deputy Manager Yongming Peng of Shandong Gold New City Mining Company, Deputy Manager Haicheng Qiu of Chifeng Gold Group, Fuxing Liu and Shenghui Li, Deputy Chief Engineer of Liaoning Nonferrous Geology 103 Team, Xiuzhang Li and Wen Zhang, Chief Engineer of Shandong Geological Survey Institute. Three anonymous reviewers and Prof. Huayong Chen, the Editorial Board Member of the Journal, put forward valuable opinions and suggestions. This study was supported by the National Key R & D Program of China (Grant Nos. 2016YFC0600107 & 2016YFC0600108).

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Correspondence to Gongwen Wang.

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Wang, G., Zhang, Z., Li, R. et al. Resource prediction and assessment based on 3D/4D big data modeling and deep integration in key ore districts of North China. Sci. China Earth Sci. 64, 1590–1606 (2021). https://doi.org/10.1007/s11430-020-9791-4

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Keywords

  • Geoscience big data
  • 3D/4D modeling
  • Weights of evidence
  • Random forest
  • Target optimization and resources assessment
  • Gold district in North China