IGGCAS OpenIR  > 矿产资源研究院重点实验室
Machine learning coupled with mineral geochemistry reveals the origin of ore deposits
Sun, Guotao1,2,3; Zeng, Qingdong4,5,6; Zhou, Jia-Xi7,8
2022-03-01
Source PublicationORE GEOLOGY REVIEWS
ISSN0169-1368
Volume142Pages:12
AbstractAs geosciences enter the era of big data, machine learning (ML) that is successful in big data, is now contributing to solving problems in the geosciences, yet there have been few applications in economic geology. This paper highlights the effectiveness of ML-based methods coupled with mineral geochemistry in revealing the origin of the Qingchengzi Pb-Zn ore field in China, which are either metamorphosed sedimentary exhalative (SEDEX) or magmatic-hydrothermal fluid related deposits. Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) pyrite trace elements coupled with decision tree (DT), K-nearest neighbors (KNN), and support vector machine (SVM) algorithms were applied to train the classification models. Testing of the DT, KNN, and SVM classifiers yielded accuracies of 98.2%, 96.4%, and 93.6%, respectively. The trained classifiers predict that the strata-bound and vein-type ore bodies at Qingchengzi ore field have a magmatic-hydrothermal origin, with DT, KNN, and SVM values of 100%, 97.4%, and 97.4%. In situ delta S-34 values of pyrite from strata-bound and veintype ore bodies are 4.04% to 9.10% and 6.31% to 9.29%, respectively, slightly higher than those of magmatic intrusions. In situ Pb isotopic ratios plot on the upper crust curve and yield two-stage model ages that are younger than metamorphic events in the region. Principal component (PC) analysis was used to determine the formation of the two types of mineralization. Pyrite from vein-type ore bodies (Py1) has lower contents of PC1 elements (Cu, Zn, Ge, Ag, Cd, Sn, Sb, and Pb) and higher contents of PC2 elements (Co, Ni, and Se) compared with pyrite from strata-bound ore bodies (Py2). Combined with previous fluid inclusion data, the vein-type ore bodies are inferred to have formed at higher temperatures than the strata-bound ore bodies. This study presents three visual classifiers to discriminate metamorphosed SEDEX and magmatic-hydrothermal Pb-Zn deposits. The prediction of classifiers and in situ S-Pb isotopic compositions suggest that the Qingchengzi Pb-Zn deposits have a magmatic-hydrothermal origin. The results demonstrate the effective application of ML-based methods to examine the origin of ore deposits.
KeywordMachine learning Pyrite trace elements In situ S-Pb isotopes Qingchengzi Pb-Zn deposits
DOI10.1016/j.oregeorev.2022.104753
Funding OrganizationApplied Basic Research Foundation of Yunnan Province ; Applied Basic Research Foundation of Yunnan Province ; National Key R&D Program of China ; National Key R&D Program of China ; State Key Laboratory of Lithospheric Evolution ; State Key Laboratory of Lithospheric Evolution ; Research Startup Project for Introduced Talent of Yunnan University ; Research Startup Project for Introduced Talent of Yunnan University ; Cultivation Project for National Excellent Youth of Yunnan University ; Cultivation Project for National Excellent Youth of Yunnan University ; Applied Basic Research Foundation of Yunnan Province ; Applied Basic Research Foundation of Yunnan Province ; National Key R&D Program of China ; National Key R&D Program of China ; State Key Laboratory of Lithospheric Evolution ; State Key Laboratory of Lithospheric Evolution ; Research Startup Project for Introduced Talent of Yunnan University ; Research Startup Project for Introduced Talent of Yunnan University ; Cultivation Project for National Excellent Youth of Yunnan University ; Cultivation Project for National Excellent Youth of Yunnan University ; Applied Basic Research Foundation of Yunnan Province ; Applied Basic Research Foundation of Yunnan Province ; National Key R&D Program of China ; National Key R&D Program of China ; State Key Laboratory of Lithospheric Evolution ; State Key Laboratory of Lithospheric Evolution ; Research Startup Project for Introduced Talent of Yunnan University ; Research Startup Project for Introduced Talent of Yunnan University ; Cultivation Project for National Excellent Youth of Yunnan University ; Cultivation Project for National Excellent Youth of Yunnan University ; Applied Basic Research Foundation of Yunnan Province ; Applied Basic Research Foundation of Yunnan Province ; National Key R&D Program of China ; National Key R&D Program of China ; State Key Laboratory of Lithospheric Evolution ; State Key Laboratory of Lithospheric Evolution ; Research Startup Project for Introduced Talent of Yunnan University ; Research Startup Project for Introduced Talent of Yunnan University ; Cultivation Project for National Excellent Youth of Yunnan University ; Cultivation Project for National Excellent Youth of Yunnan University
WOS KeywordZIRCON U-PB ; NORTH CHINA CRATON ; PALEOPROTEROZOIC CRUSTAL EVOLUTION ; HF ISOTOPIC COMPOSITIONS ; TRACE-ELEMENT CONTENT ; SEDIMENTARY PYRITE ; EASTERN BLOCK ; LIAODONG PENINSULA ; LIAONING PROVINCE ; CONSTRAINTS
Language英语
Funding ProjectApplied Basic Research Foundation of Yunnan Province[202001BB050020] ; National Key R&D Program of China[2016YFC0600108] ; State Key Laboratory of Lithospheric Evolution[S201605] ; Research Startup Project for Introduced Talent of Yunnan University[YJRC4201804] ; Cultivation Project for National Excellent Youth of Yunnan University[2018YDJQ009]
Funding OrganizationApplied Basic Research Foundation of Yunnan Province ; Applied Basic Research Foundation of Yunnan Province ; National Key R&D Program of China ; National Key R&D Program of China ; State Key Laboratory of Lithospheric Evolution ; State Key Laboratory of Lithospheric Evolution ; Research Startup Project for Introduced Talent of Yunnan University ; Research Startup Project for Introduced Talent of Yunnan University ; Cultivation Project for National Excellent Youth of Yunnan University ; Cultivation Project for National Excellent Youth of Yunnan University ; Applied Basic Research Foundation of Yunnan Province ; Applied Basic Research Foundation of Yunnan Province ; National Key R&D Program of China ; National Key R&D Program of China ; State Key Laboratory of Lithospheric Evolution ; State Key Laboratory of Lithospheric Evolution ; Research Startup Project for Introduced Talent of Yunnan University ; Research Startup Project for Introduced Talent of Yunnan University ; Cultivation Project for National Excellent Youth of Yunnan University ; Cultivation Project for National Excellent Youth of Yunnan University ; Applied Basic Research Foundation of Yunnan Province ; Applied Basic Research Foundation of Yunnan Province ; National Key R&D Program of China ; National Key R&D Program of China ; State Key Laboratory of Lithospheric Evolution ; State Key Laboratory of Lithospheric Evolution ; Research Startup Project for Introduced Talent of Yunnan University ; Research Startup Project for Introduced Talent of Yunnan University ; Cultivation Project for National Excellent Youth of Yunnan University ; Cultivation Project for National Excellent Youth of Yunnan University ; Applied Basic Research Foundation of Yunnan Province ; Applied Basic Research Foundation of Yunnan Province ; National Key R&D Program of China ; National Key R&D Program of China ; State Key Laboratory of Lithospheric Evolution ; State Key Laboratory of Lithospheric Evolution ; Research Startup Project for Introduced Talent of Yunnan University ; Research Startup Project for Introduced Talent of Yunnan University ; Cultivation Project for National Excellent Youth of Yunnan University ; Cultivation Project for National Excellent Youth of Yunnan University
WOS Research AreaGeology ; Mineralogy ; Mining & Mineral Processing
WOS SubjectGeology ; Mineralogy ; Mining & Mineral Processing
WOS IDWOS:000766746600002
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.iggcas.ac.cn/handle/132A11/104990
Collection矿产资源研究院重点实验室
Corresponding AuthorSun, Guotao
Affiliation1.Guizhou Univ, Coll Resources & Environm Engn, Guiyang 550025, Peoples R China
2.Guizhou Univ, State Key Lab Publ Big Data, Guiyang 550025, Peoples R China
3.Minist Educ, Key Lab Karst Georesources & Environm, Guiyang 550025, Peoples R China
4.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Mineral Resources, Beijing 100029, Peoples R China
5.Chinese Acad Sci, Inst Earth Sci, Beijing 100029, Peoples R China
6.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
7.Yunnan Univ, Sch Earth Sci, Kunming 650500, Yunnan, Peoples R China
8.Key Lab Crit Minerals Metallogeny Univ Yunnan Pro, Kunming 650500, Yunnan, Peoples R China
Recommended Citation
GB/T 7714
Sun, Guotao,Zeng, Qingdong,Zhou, Jia-Xi. Machine learning coupled with mineral geochemistry reveals the origin of ore deposits[J]. ORE GEOLOGY REVIEWS,2022,142:12.
APA Sun, Guotao,Zeng, Qingdong,&Zhou, Jia-Xi.(2022).Machine learning coupled with mineral geochemistry reveals the origin of ore deposits.ORE GEOLOGY REVIEWS,142,12.
MLA Sun, Guotao,et al."Machine learning coupled with mineral geochemistry reveals the origin of ore deposits".ORE GEOLOGY REVIEWS 142(2022):12.
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