Deposit type discrimination of Jiaodong gold deposits using random forest algorithm: Constraints from trace elements of pyrite

[Display omitted] •Jiaojia- and Linglong- type gold deposits may share a common source of ore-forming fluids.•Bi, Zn, and As exhibit significant differences in the pyrite of the two types of gold deposits.•The use of pyrite trace elements and random forest algorithm can effectively distinguish betwe...

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Published inOre geology reviews Vol. 175; p. 106343
Main Authors Chen, Yang, Li, Tongfei, Fu, Bin, Xia, Qinglin, Liu, Qiankun, Li, Taotao, Yang, Yizeng, Huang, Yufeng
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.12.2024
Subjects
Factor analysis
Jiaodong gold deposit
Pyrite trace element
Random forest
Pyrite trace element
Jiaodong gold deposit
Random forest
Factor analysis
Online AccessGet full text
ISSN0169-1368
DOI10.1016/j.oregeorev.2024.106343

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Abstract [Display omitted] •Jiaojia- and Linglong- type gold deposits may share a common source of ore-forming fluids.•Bi, Zn, and As exhibit significant differences in the pyrite of the two types of gold deposits.•The use of pyrite trace elements and random forest algorithm can effectively distinguish between these two types of gold deposits. A significant amount of gold is produced in Jiaodong Peninsula, North China. The Jiaojia-type (fracture-disseminated rock type) and Linglong-type (sulfide-bearing quartz vein type) are the most two important types of gold deposits related to hydrothermal fluids in this region. Therefore, understanding the differences in ore-forming fluids between these two types of gold deposits is crucial for genesis and exploration, yet there is a lack of comprehensive documentation on this subject. As an important gold-bearing mineral, pyrite plays a significant role in revealing the characteristics of ore-forming fluids. In this paper, the big data analysis and machine learning methods are applied to discriminate the types of the gold deposits. The factor analysis (FA) and the random forest (RF) algorithm to examine the presence of trace elements of pyrite in Jiaojia- and Linglong-type gold deposits. The FA analysis reveals that the elements in pyrite can be grouped into four factors: F1 (Ag-Pb-Bi), F2 (Cu-Zn), F3 (Co-Ni), and F4 (Au-As). This classification is likely influenced by the distribution of trace elements within pyrite. The interconnectedness among the F1-F2-F3-F4 components implies a common source of ore-forming fluids between these two gold deposit types. At the same time, the random forest model highlights Bi, Zn, and As as the most distinguishing elements in pyrite between the two deposit types. These findings suggest that Jiaojia- and Linglong-type gold deposits have distinct temperatures of the ore-forming fluids and at the extension of the ore-controlling structure of Jiaojia-type ore body may exist the Linglong-type ore body. Accordingly, a machine learning model was developed for detecting the two types of gold deposits. This pioneering research blends big data analytics and artificial intelligence to enhance the classification of mineral deposits, offering a novel approach to mineral exploration in the Jiaodong region.
AbstractList [Display omitted] •Jiaojia- and Linglong- type gold deposits may share a common source of ore-forming fluids.•Bi, Zn, and As exhibit significant differences in the pyrite of the two types of gold deposits.•The use of pyrite trace elements and random forest algorithm can effectively distinguish between these two types of gold deposits. A significant amount of gold is produced in Jiaodong Peninsula, North China. The Jiaojia-type (fracture-disseminated rock type) and Linglong-type (sulfide-bearing quartz vein type) are the most two important types of gold deposits related to hydrothermal fluids in this region. Therefore, understanding the differences in ore-forming fluids between these two types of gold deposits is crucial for genesis and exploration, yet there is a lack of comprehensive documentation on this subject. As an important gold-bearing mineral, pyrite plays a significant role in revealing the characteristics of ore-forming fluids. In this paper, the big data analysis and machine learning methods are applied to discriminate the types of the gold deposits. The factor analysis (FA) and the random forest (RF) algorithm to examine the presence of trace elements of pyrite in Jiaojia- and Linglong-type gold deposits. The FA analysis reveals that the elements in pyrite can be grouped into four factors: F1 (Ag-Pb-Bi), F2 (Cu-Zn), F3 (Co-Ni), and F4 (Au-As). This classification is likely influenced by the distribution of trace elements within pyrite. The interconnectedness among the F1-F2-F3-F4 components implies a common source of ore-forming fluids between these two gold deposit types. At the same time, the random forest model highlights Bi, Zn, and As as the most distinguishing elements in pyrite between the two deposit types. These findings suggest that Jiaojia- and Linglong-type gold deposits have distinct temperatures of the ore-forming fluids and at the extension of the ore-controlling structure of Jiaojia-type ore body may exist the Linglong-type ore body. Accordingly, a machine learning model was developed for detecting the two types of gold deposits. This pioneering research blends big data analytics and artificial intelligence to enhance the classification of mineral deposits, offering a novel approach to mineral exploration in the Jiaodong region.
ArticleNumber 106343
Author Liu, Qiankun
Huang, Yufeng
Fu, Bin
Xia, Qinglin
Li, Tongfei
Yang, Yizeng
Chen, Yang
Li, Taotao
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  fullname: Chen, Yang
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  givenname: Tongfei
  surname: Li
  fullname: Li, Tongfei
  email: 23000032@wxc.edu.cn
  organization: College of Environment and Tourism, West Anhui University, Lu’an 237012, China
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  givenname: Bin
  surname: Fu
  fullname: Fu, Bin
  organization: State Key Laboratory of Ore Deposit Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, China
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  givenname: Qinglin
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  organization: College of Environment and Tourism, West Anhui University, Lu’an 237012, China
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Keywords Pyrite trace element
Jiaodong gold deposit
Random forest
Factor analysis
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Snippet [Display omitted] •Jiaojia- and Linglong- type gold deposits may share a common source of ore-forming fluids.•Bi, Zn, and As exhibit significant differences in...
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SubjectTerms Factor analysis
Jiaodong gold deposit
Pyrite trace element
Random forest
Title Deposit type discrimination of Jiaodong gold deposits using random forest algorithm: Constraints from trace elements of pyrite
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