Earth Science Frontiers ›› 2025, Vol. 32 ›› Issue (4): 280-290.DOI: 10.13745/j.esf.sf.2025.4.64

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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

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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