Machine learning for deciphering ore-forming fluid sources using scheelite trace element geochemistry
文献类型：期刊论文
| 作者 | Zhao, Hongtao3; Liu, Mingrui3; Zhang, Yu3,4; Shao, Yongjun3; Yu, Zequn3; Cao, Genshen1; Zhao, Lianjie; Li, Yongshun3 |
| 刊名 | ORE GEOLOGY REVIEWS
 |
| 出版日期 | 2024-12-01 |
| 卷号 | 175页码:15 |
| 关键词 | Scheelite Trace element geochemistry Fluid sources Machine learning Xiangzhong metallogenic province |
| ISSN号 | 0169-1368 |
| DOI | 10.1016/j.oregeorev.2024.106374 |
| 英文摘要 | Identifying the source of ore-forming fluids is crucial for constraining ore genesis and guiding exploration. This study introduces a novel approach that leverages the geochemical properties of scheelite and the latest advancements in machine learning algorithms to decipher ore-forming fluid sources. A variety of supervised machine learning methods, including Decision Tree, Random Forest, Multilayer Perceptron, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), k-Nearest Neighbors, and Logistic Regression, are employed to identify the source of scheelite ore-forming fluids using high-dimensional information of scheelite trace element data. This study demonstrates that XGBoost (accuracy: 93.5%, AUC: 98.8%) and LightGBM (accuracy: 93.2%, AUC: 98.6%) classifiers efficiently and accurately classify high-dimensional trace element data of metamorphic-hydrothermal and magmatic-hydrothermal scheelite. Interpretation of the models using the SHapley Additive exPlanations tool reveals that Sr, La, Eu, Nb, Pb, Ta, and Mo of scheelite are the most indicative elements for predicting ore-forming fluid sources. Additionally, the discrimination of scheelite data by the XGBoost and LightGBM algorithms suggests that the Darongxi W, Muguayuan W, Yangjiashan Au-Sb-W, and Longshan Au-Sb-W deposits in the Xiangzhong metallogenic province (XZMP, South China) are likely magmaticrelated, while the Daping Au, Woxi Au-Sb-W, and Zhazixi Au-Sb-W deposits are likely orogenic. This reveals the complexity of regional Au-Sb-W mineralization in the XZMP. Importantly, this research highlights the untapped potential of integrating scheelite trace element geochemical data with explainable machine learning technology to determine ore-forming fluid sources. |
| WOS研究方向 | Geology ; Mineralogy ; Mining & Mineral Processing |
| 语种 | 英语 |
| WOS记录号 | WOS:001373057800001 |
| 源URL | [http://ir.gig.ac.cn/handle/344008/81956]  |
| 专题 | 中国科学院矿物学与成矿学重点实验室 |
| 通讯作者 | Zhang, Yu |
| 作者单位 | 1.Chinese Acad Sci, Guangzhou Inst Geochem, Key Lab Mineral & Metallogeny, Guangzhou 510640, Peoples R China 2.Shandong Univ Technol, Sch Resources & Environm Engn, Zibo 255049, Peoples R China 3.Cent South Univ, Key Lab Metallogen Predict Nonferrous Met & Geol E, Minist Educ, Changsha 410083, Peoples R China 4.Cent South Univ, Sch Geosci & Info Phys, Changsha 410083, Peoples R China 5.Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Hongtao,Liu, Mingrui,Zhang, Yu,et al. Machine learning for deciphering ore-forming fluid sources using scheelite trace element geochemistry[J]. ORE GEOLOGY REVIEWS,2024,175:15. |
| APA | Zhao, Hongtao.,Liu, Mingrui.,Zhang, Yu.,Shao, Yongjun.,Yu, Zequn.,...&Li, Yongshun.(2024).Machine learning for deciphering ore-forming fluid sources using scheelite trace element geochemistry.ORE GEOLOGY REVIEWS,175,15. |
| MLA | Zhao, Hongtao,et al."Machine learning for deciphering ore-forming fluid sources using scheelite trace element geochemistry".ORE GEOLOGY REVIEWS 175(2024):15. |
入库方式： OAI收割
来源：广州地球化学研究所
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