气候变化领域集成服务门户(CCPortal): GIS-based mineral prospectivity mapping using machine learning methods: A case study from Zhuonuo ore district, Tibet

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DOI	10.1016/j.oregeorev.2023.105627
	GIS-based mineral prospectivity mapping using machine learning methods: A case study from Zhuonuo ore district, Tibet
	Cheng, Hongjun; Zheng, Youye; Wu, Song; Lin, Yibin; Gao, Feng; Lin, Decai; Wei, Jiangang; Wang, Shucheng; Shu, Defu; Wei, Shoucai; Chen, Lie
发表日期	2023
ISSN	0169-1368
EISSN	1872-7360
卷号	161
英文摘要	The Zhunuo ore concentration area (ZOCA) is the most potential prospective area of Cu-Au (Mo) in the west of the southern subterrane, Tibet. Single traditional prospective methods (e.g., stream sedimentary geochemistry) often produced larger area and false abnormal information in the Gangdese orogenic belt because of the high altitude and the intense weather and erosion, which can not meet the urgent demand of the current situation for Cu resources. In this study, we combined a mineral system approach with GIS-based machine learning approachs to obtain geologically meaningful mineral prospective maps. The detail steps include: (i) establishing the mineral system conception model of porphyry copper deposits (PCDs); (ii) transforming the targeted porphyry metallogenic system components into spatial proxies associated with the crucial ore-forming processes; (iii) extracting the spatial proxies: proximity to intrusive rocks (source), NE orientation faults (transport and/or physical trap), Fe-oxide and propylitization hydrothermal alterations zone (hydrothermal fluids) and the metallogenic strength diagram of Cu-Mo-W-Bi-Au-Ag-Pb-Zn (deposition); (iv) Radial Basis Functions Link Networks (RBFLN), Random forests (RF) Supervised and Fuzzy Clustering (FC) unsupervised machine learning methods were applied to capture the complex and crucial mineralization information between known deposit types and evidence layers; (vi) model estimation and delineating prospective potential targets: Receiver operating characteristic curve (ROC), predictive-area (P-A) plotting and normalised density (Nd) were used to evaluate the predictive models results. The results indicate that the RBFLN model, RF model, and FC model show high predictive accuracy. The AUC values under the ROC area of the RBFLN model, RF model, and FC model are 0.99, 0.96, and 0.94, respectively. The RBFLN model outperforms the RF model and FC model, the predictive-area plotting of RBFLN occupies 12% of the study area containing 88% of the known deposits. The predictive-area plotting of the RF model and FC model showed that 14% and 21% of the study area contained 86% and 79% of the known deposits, respectively. The normalized density (Nd) of a layer is defined as the ratio of the prediction success rate (Pr) of the P-A plotting to the corresponding area (Oa). The normalized density of the RBFLN model, the RF model, and the FC model are 7.33, 6.14, and 3.76, respectively, which revealed that the results of the three predictive models all have positive indications. These studies show that RBFLN supervised machine learning method is a more robustness and generalization capability. The predictive results also provide prospective potential targets (e.g., northern Cimabanshuo, northwest Wubaduolai, and southwestern and western Zhunuo PCD) for further exploration, and this method can be also applicable to other mineral systems and districts.
关键词	Porphyry metallogenic system RBFLN Gangdese polymetallic belt Mineral prospectivity mapping Machine learning Random forests
英文关键词	PORPHYRY CU-MO; ZIRCON U-PB; HF ISOTOPIC CONSTRAINTS; EASTERN GANGDESE BELT; BIG DATA ANALYTICS; NEURAL-NETWORKS; CONTINENTAL COLLISION; MODEL SELECTION; SOUTHERN TIBET; RANDOM FOREST
WOS研究方向	Geology ; Mineralogy ; Mining & Mineral Processing
WOS记录号	WOS:001087572000001
来源期刊	ORE GEOLOGY REVIEWS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/282815
作者单位	China University of Geosciences - Beijing; China University of Geosciences - Beijing; China University of Geosciences - Wuhan
推荐引用方式 GB/T 7714	Cheng, Hongjun,Zheng, Youye,Wu, Song,et al. GIS-based mineral prospectivity mapping using machine learning methods: A case study from Zhuonuo ore district, Tibet[J],2023,161.
APA	Cheng, Hongjun.,Zheng, Youye.,Wu, Song.,Lin, Yibin.,Gao, Feng.,...&Chen, Lie.(2023).GIS-based mineral prospectivity mapping using machine learning methods: A case study from Zhuonuo ore district, Tibet.ORE GEOLOGY REVIEWS,161.
MLA	Cheng, Hongjun,et al."GIS-based mineral prospectivity mapping using machine learning methods: A case study from Zhuonuo ore district, Tibet".ORE GEOLOGY REVIEWS 161(2023).