Home Journals Articles Updates About
CN
image
收藏切换
Identifying Porphyry Copper Ore-Forming Intrusions Using Biotite as An Indicator Mineral: A Machine Learning Perspective
收藏切换
Chuijin KONG1, 2, 3, Hui YUAN1, 2, 3, Zhiqing LAI1, 2, 3, Sanzhong LI1, 2, 3, Shihua ZHONG1, 2, 3, *
Northwestern Geology | 2026, 59(4) : 48 - 62
Less
收藏切换
Northwestern Geology | 2026, 59(4): 48-62
Identifying Porphyry Copper Ore-Forming Intrusions Using Biotite as An Indicator Mineral: A Machine Learning Perspective
Full
Chuijin KONG1, 2, 3, Hui YUAN1, 2, 3, Zhiqing LAI1, 2, 3, Sanzhong LI1, 2, 3, Shihua ZHONG1, 2, 3, *
Affiliations
  • 1College of Marine Geosciences, Ocean University of China, Qingdao 266100, Shandong, China
  • 2Key Laboratory of Submarine Geosciences and Prospecting Techniques, Ministry of Education, Qingdao 266100, Shandong, China
  • 3Laboratory for Marine Mineral Resources, Qingdao Marine Science and Technology Center, Qingdao 266237, Shandong, China
Published: 2026-08-20 doi: 10.12401/j.nwg.2025189
Outline
收藏切换

Accurate identification of ore-forming intrusions is critical for porphyry copper exploration. Previous studies have extensively used trace-element compositions of magmatic accessory minerals, such as zircon and apatite, to evaluate the metallogenic potential of magmas. However, the trace-element compositions of accessory minerals are controlled by multiple factors, which may limit their effectiveness in recording ore-forming magma signatures. Biotite is a common rock-forming mineral in intermediate to felsic igneous rocks, and its major-element compositions are mainly controlled by the parental magma, making it a potential indicator mineral for magmatic metallogenic potential. Based on a systematic compilation of biotite data from porphyry copper ore-forming intrusions and barren intermediate to felsic intrusions worldwide, this study selected nine major elements of biotite to construct three machine-learning models, namely support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB), for identifying porphyry copper ore-forming intrusions. The results show that all three models perform well, with accuracies of 0.9561, 0.9614, and 0.9587, respectively, indicating that this approach can effectively identify ore-forming intrusions related to porphyry copper mineralization. SHAP analysis further reveals that FeOT, Na2O, and MnO are the three most important features controlling model outputs, and that biotite from ore-forming intrusions is generally characterized by low FeOT, low MnO, and high Na2O. These results demonstrate that the major-element compositions of biotite can effectively indicate the metallogenic potential of intrusions, providing a new approach for identifying ore-forming intrusions in porphyry copper systems and for guiding exploration of similar deposit types.

porphyry copper deposit  /  biotite  /  major elements  /  machine learning  /  ore-forming intrusion identification
孔垂瑾, 袁辉, 来志庆, 李三忠, 钟世华. 运用“指针矿物”黑云母识别斑岩铜矿成矿岩体：机器学习视角. 西北地质, 2026 , 59 (4) : 48 -62 . DOI: 10.12401/j.nwg.2025189
Chuijin KONG, Hui YUAN, Zhiqing LAI, Sanzhong LI, Shihua ZHONG. Identifying Porphyry Copper Ore-Forming Intrusions Using Biotite as An Indicator Mineral: A Machine Learning Perspective[J]. Northwestern Geology, 2026 , 59 (4) : 48 -62 . DOI: 10.12401/j.nwg.2025189
Appendix
Less
Year 2026 volume 59 Issue 4
138
60
Cite this Article
BibTeX
Article Info
doi: 10.12401/j.nwg.2025189
  • Receive Date：2026-03-13
  • Online Date：2026-09-16
  • Published：2026-08-20
Article Data
Affiliations
History
  • Received：2026-03-13
  • Revised：2026-04-10
  • Accepted：2026-04-10
Affiliations
    1College of Marine Geosciences, Ocean University of China, Qingdao 266100, Shandong, China
    2Key Laboratory of Submarine Geosciences and Prospecting Techniques, Ministry of Education, Qingdao 266100, Shandong, China
    3Laboratory for Marine Mineral Resources, Qingdao Marine Science and Technology Center, Qingdao 266237, Shandong, China
References
Share
https://castjournals.cast.org.cn/joweb/xbdz/EN/10.12401/j.nwg.2025189
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House