地学前缘 ›› 2025, Vol. 32 ›› Issue (4): 280-290.DOI: 10.13745/j.esf.sf.2025.4.64

• 人工智能驱动地学知识发现 • 上一篇    下一篇

基于知识图谱的斑岩型铜矿预测研究进展

董宇浩1(), 王永志1,2,3,*(), 田江涛3, 王成3, 温世博1, 李博文2   

  1. 1.吉林大学 综合信息矿产预测研究所, 吉林 长春 130061
    2.吉林大学 地球探测科学与技术学院, 吉林 长春 130061
    3.新疆维吾尔自治区地质研究院, 新疆 乌鲁木齐 830057
  • 收稿日期:2024-11-01 修回日期:2025-03-10 出版日期:2025-07-25 发布日期:2025-08-04
  • 通信作者: *王永志(1974—),男,博士,教授,主要从事地球科学大数据分析与挖掘、矿产资源智能预测等理论与应用研究工作。E-mail: wangyongzhi@jlu.edu.cn
  • 作者简介:董宇浩(1999—),男,硕士研究生,地球探测与信息技术专业,从事大数据与人工智能驱动下的矿产资源预测研究。E-mail: dongyh23@mails.jlu.edu.cn
  • 基金资助:
    自然科学基金重点项目“重要铜-金矿床类型成矿机制与勘查标识大数据综合研究”(42230810);国家重点研发计划项目“大数据和人工智能驱动的全球关键矿产资源潜力评价技术”(2021YFC2901801);国家重点研发计划项目“滨海地区金矿多元信息综合预测与潜力评价”(2023YFC2906903);国家重点研发计划项目“混场源多参数航空电磁勘探大数据分析”(2023YFC2907105);自然资源部新一轮找矿突破战略行动科技支撑项目“隐伏矿体找矿模型构建与精准定位技术”(ZKKJ202419);新疆维吾尔自治区重大科技专项“中-巴经济走廊铜稀有等战略性矿产资源成矿预测与潜力评价”(2022A03010-4)

Research progress on porphyry copper deposit prediction based on knowledge graphs

DONG Yuhao1(), WANG Yongzhi1,2,3,*(), TIAN Jiangtao3, WANG Cheng3, WEN Shibo1, LI Bowen2   

  1. 1. Institute of Integrated Information for Mineral Resources Prediction, Jilin University, Changchun 130061, China
    2. College of Geoexploration Science and Technology, Jilin University, Changchun 130061, China
    3. Xinjiang Academy of Geological Research, ürümqi 830057, China
  • Received:2024-11-01 Revised:2025-03-10 Online:2025-07-25 Published:2025-08-04

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摘要：

铜是我国对外依存较高的金属资源,而斑岩铜矿是铜矿中最重要的矿床类型之一。为系统分析斑岩铜矿预测领域的研究现状、热点和前沿趋势,本研究以CNKI(中国知网)和WoS(Web of Science)数据库收录的1980—2024年斑岩铜矿预测相关文献为样本,采用CiteSpace和VOSviewer软件进行知识图谱构建和数据信息挖掘。通过国家发文情况、国际国内作者和机构、关键词等多维度解析,结果显示:(1)全球范围内,伊朗和中国在该领域研究最为活跃,发文量约占总发文量的50%,而国内已形成以成都理工大学、中国地质大学(北京)为核心的研究机构群;(2)作者合作关系网络和共被引分析显示国内外核心作者群体有待形成,但跨区域合作网络已显现集聚效应,研究正向系统化发展;(3)关键词聚类分析中,CNKI和WoS分别识别出11个聚类和16个聚类,突现图谱和云图分析显示成矿条件与规律、地质特征和地球化学等共同构成研究主轴且具成熟性,机器学习和知识图谱是新型技术增长点。研究构建的领域知识图谱可为斑岩铜矿预测研究提供全景式认知框架,也可为深部找矿技术创新和勘查战略制定提供一定理论支撑。

关键词: 斑岩型, 铜矿预测, 知识图谱, 研究趋势

Abstract:

Copper is a metal resource with high external dependence in China, and porphyry copper deposits represent one of the most critical copper deposit types. To systematically analyze the research status, hotspots, and frontier trends in porphyry copper deposit prediction, this study utilizes literature samples from the CNKI (China National Knowledge Infrastructure) and Web of Science (WoS) databases spanning 1980-2024. Knowledge graph construction and data mining were conducted using CiteSpace and VOSviewer. Through multidimensional analyses of national publication outputs, author-institutional collaborations, and keyword evolution, the results reveal: (1) Iran and China are the most active contributors globally, accounting for approximately 50% of total publications, with Chengdu University of Technology and China University of Geosciences (Beijing) emerging as core research institutions in China. (2) Author collaboration networks and co-citation analyses indicate that core author groups remain underdeveloped both domestically and internationally, yet cross-regional collaborative networks have demonstrated clustering effects, driving research toward systematization. (3) Keyword clustering identifies 11 knowledge modules, while burst detection and visualization analyses highlight “metallogenic conditions and regularities, geological characteristics, and geochemistry” as mature research pillars, whereas “machine learning” and “knowledge graph” represent emerging technological frontiers. The constructed domain-specific knowledge graph provides a panoramic framework for understanding porphyry copper deposit prediction and offers theoretical insights for deep mineral exploration innovation and strategic decision-making.

Key words: porphyry type, copper prediction, knowledge graphs, research trends

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