Resource prediction and assessment based on 3D/4D big data modeling and deep integration in key ore districts of North China
文献类型：期刊论文
| 作者 | Wang, Gongwen8; Zhang, Zhiqiang8; Li, Ruixi8; Li, Junjian7; Sha, Deming6; Zeng, Qingdong1,4,5; Pang, Zhenshan3; Li, Dapeng2; Huang, Leilei8 |
| 刊名 | SCIENCE CHINA-EARTH SCIENCES
 |
| 出版日期 | 2021-07-23 |
| 页码 | 17 |
| 关键词 | Geoscience big data 3D 4D modeling Weights of evidence Random forest Target optimization and resources assessment Gold district in North China |
| ISSN号 | 1674-7313 |
| DOI | 10.1007/s11430-020-9791-4 |
| 英文摘要 | 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. |
| WOS关键词 | ZIRCON U-PB ; LIAO-JI BELT ; NORTHWESTERN JIAODONG PENINSULA ; XINCHENG GOLD DEPOSIT ; OROGENIC GOLD ; TECTONIC IMPLICATIONS ; JIAOBEI TERRANE ; EASTERN BLOCK ; NE CHINA ; CONSTRAINTS |
| 资助项目 | National Key R&D Program of China[2016YFC0600107] ; National Key R&D Program of China[2016YFC0600108] |
| WOS研究方向 | Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:000677930100001 |
| 出版者 | SCIENCE PRESS |
| 资助机构 | National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China |
| 源URL | [http://ir.iggcas.ac.cn/handle/132A11/101935]  |
| 专题 | 地质与地球物理研究所_中国科学院矿产资源研究重点实验室 |
| 通讯作者 | Wang, Gongwen |
| 作者单位 | 1.Chinese Acad Sci, Inst Earth Sci, Beijing 100029, Peoples R China 2.Shandong Acad Geol Sci, Jinan 250013, Peoples R China 3.China Geol Survey, Dev Res Ctr, Beijing 100037, Peoples R China 4.Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Mineral Resources, Beijing 100029, Peoples R China 6.China Geol Survey, Shenyang Geol Survey Ctr, Shenyang 110000, Peoples R China 7.China Geol Survey, Tianjin Geol Survey Ctr, Tianjin 300170, Peoples R China 8.China Univ Geosci Beijing, Sch Earth Sci & Resources, Beijing 100083, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Gongwen,Zhang, Zhiqiang,Li, Ruixi,et al. Resource prediction and assessment based on 3D/4D big data modeling and deep integration in key ore districts of North China[J]. SCIENCE CHINA-EARTH SCIENCES,2021:17. |
| APA | Wang, Gongwen.,Zhang, Zhiqiang.,Li, Ruixi.,Li, Junjian.,Sha, Deming.,...&Huang, Leilei.(2021).Resource prediction and assessment based on 3D/4D big data modeling and deep integration in key ore districts of North China.SCIENCE CHINA-EARTH SCIENCES,17. |
| MLA | Wang, Gongwen,et al."Resource prediction and assessment based on 3D/4D big data modeling and deep integration in key ore districts of North China".SCIENCE CHINA-EARTH SCIENCES (2021):17. |
入库方式： OAI收割
来源：地质与地球物理研究所
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