地学前缘 ›› 2025, Vol. 32 ›› Issue (4): 262-279.DOI: 10.13745/j.esf.sf.2025.3.31

• 人工智能驱动地学知识发现 • 上一篇    下一篇

基于知识图谱的碳酸岩型稀土矿成矿要素挖掘

冯婷婷(), 蔡诗柔, 张振杰*()   

  1. 中国地质大学(北京)深时数字地球前沿科学中心, 地质过程与成矿预测全国重点实验室, 地球科学与资源学院, 北京 100083
  • 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: *张振杰(1988—),男,博士,副教授,博士生导师,矿产普查与勘探专业,主要从事地学大数据和矿产预测研究。E-mail: zjzhang@cugb.edu.cn
  • 作者简介:冯婷婷(1999—),女,硕士研究生,资源与环境专业。E-mail: cnfting.en@foxmail.com
  • 基金资助:
    国家重点研发计划项目(2023YFC2906402);国家自然科学基金项目(42430111);国家自然科学基金项目(42472358);国家自然科学基金项目(42050103);中央高校基本科研业务费(2652023001)

Mining elements of carbonatite-type rare earth deposits based on knowledge map

FENG Tingting(), CAI Shirou, ZHANG Zhenjie*()   

  1. Frontiers Science Center for Deep-time Digital Earth, State Key Laboratory of Geological Processes and Mineral Resources, School of Earth Sciences and Resources, China University of Geosciences(Beijing), Beijing 100083, China
  • Received:2024-11-01 Revised:2025-03-10 Online:2025-07-25 Published:2025-08-04

摘要:

稀土元素在现代高科技产业中发挥着关键作用,而碳酸岩型稀土矿作为重要的资源类型,其成矿特征和机制尚不明确,严重制约了找矿勘查的突破。随着地学大数据时代的到来,知识图谱技术在矿产资源预测中的应用逐渐成为热点。本研究结合自然语言处理技术和知识图谱构建方法,针对碳酸岩型稀土矿床的成矿特征和机制开展系统研究。通过收集白云鄂博矿床和冕宁—德昌成矿带的相关文献,利用BERT-BiLSTM-CRF实体识别模型和BERT关系抽取模型构建了碳酸岩型稀土矿床领域的知识图谱。研究结果表明,矿物、岩石和元素是成矿的关键节点,其中萤石表现出较强的一致性,具有显著的指示矿物潜力;铕和铈元素因其氧化还原异常显著关联,是重要的找矿指标。此外,不同区域知识图谱反映了成矿类型的差异,白云鄂博矿床以碳酸岩为主,而冕宁—德昌成矿带与碱性岩关系密切。图谱的层次聚类分析进一步揭示了成矿要素间的关联性,为稀土矿床的成矿环境和成矿机理研究提供了新的视角,同时也为找矿预测和资源评价提供了科学依据。

关键词: 知识图谱, 碳酸岩, 稀土矿, 白云鄂博, 冕宁—德昌

Abstract:

Rare earth elements (REEs) are critical for modern high-tech industries, yet the metallogenic characteristics and mechanisms of carbonatite-hosted REE deposits remain poorly understood, significantly hindering exploration breakthroughs. With the advent of the geoscience big data era, knowledge graph technology has emerged as a key tool for mineral resource prediction. This study integrates natural language processing (NLP) techniques and knowledge graph construction methods to systematically investigate the metallogenic characteristics and mechanisms of carbonatite-hosted REE deposits. We collected literature pertaining to the Bayan Obo deposit and the Mianning-Dechang metallogenic belt. Using the BERT-BiLSTM-CRF model for entity recognition and the BERT model for relationship extraction, we constructed a domain-specific knowledge graph. Results indicate that minerals, rocks, and elements are critical nodes influencing mineralization. Fluorite exhibits high consistency across regional knowledge graphs, highlighting its potential as a prospecting indicator mineral. Europium and cerium, due to their redox-sensitive anomalies, serve as important indicators for REE exploration. Calcite and bastnaesite also demonstrate indicative potential. The knowledge graph reveals that Bayan Obo exhibits stronger associations with carbonatites, while the Mianning-Dechang belt is more closely linked to alkaline rocks. Hierarchical clustering further demonstrates significant correlations among similar nodes, providing key insights into the metallogenic environment and critical elements. This study offers a novel perspective and methodology for understanding metallogenic mechanisms and provides robust scientific support for the exploration and evaluation of REE deposits.

Key words: knowledge graph, carbonatite, rare earth deposits, Bayan Obo, Mianning-Dechang

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