地学前缘 ›› 2024, Vol. 31 ›› Issue (4): 7-15.DOI: 10.13745/j.esf.sf.2024.5.2

• 知识图谱与智能推理 • 上一篇    下一篇

找矿知识图谱的智能化应用:以钦杭成矿带斑岩铜矿为例

张前龙1,2,3(), 周永章1,2,3, 郭兰萱4, 原桂强4, 虞鹏鹏1,2,3,*(), 王汉雨1,2,3, 朱彪彪1,2,3, 韩枫1,2,3, 龙师尧1,2,3   

  1. 1.中山大学 地球科学与工程学院, 广东 珠海 519000
    2.中山大学 地球环境与资源研究中心, 广东 珠海 519000
    3.中山大学 广东省地质过程与矿产资源探查重点实验室, 广东 珠海 519000
    4.深圳市中金岭南有色金属股份有限公司, 广东 深圳 518000
  • 收稿日期:2023-09-18 修回日期:2024-02-21 出版日期:2024-07-25 发布日期:2024-07-10
  • 通信作者: * 虞鹏鹏(1991—),男,博士,副教授,主要从事造山带演化与成岩成矿作用研究工作。E-mail: yupp3@mail.sysu.edu.cn
  • 作者简介:张前龙(1997—),男,博士研究生,主要从事大数据与矿床知识图谱研究工作。E-mail: zhangqlong3@mail2.sysu.edu.cn
  • 基金资助:
    国家重点研发计划项目(2022YFF0801201);国家自然科学基金联合基金重点项目(U1911202);广东省科学技术厅科技项目(GDKTP2020053500);广东省引进人才创新创业团队项目(2021ZT09H399);广东省自然科学基金青年提升项目(2024A1515030216)

Intelligent application of knowledge graphs in mineral prospecting: A case study of porphyry copper deposits in the Qin-Hang metallogenic belt

ZHANG Qianlong1,2,3(), ZHOU Yongzhang1,2,3, GUO Lanxuan4, YUAN Guiqiang4, YU Pengpeng1,2,3,*(), WANG Hanyu1,2,3, ZHU Biaobiao1,2,3, HAN Feng1,2,3, LONG Shiyao1,2,3   

  1. 1. School of Earth Sciences and Engineering, Sun Yat-sen University, Zhuhai 519000, China
    2. Center for Earth Environment & Resources, Sun Yat-sen University, Zhuhai 519000, China
    3. Guangdong Provincial Key Laboratory of Mineral Resources and Geological Processes, Sun Yat-sen University, Zhuhai 519000, China
    4. Shenzhen Zhongjin Lingnan Non-ferrous metal Company Limited, Shenzhen 518000, China
  • Received:2023-09-18 Revised:2024-02-21 Online:2024-07-25 Published:2024-07-10

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摘要：

找矿知识图谱是一个结构化的找矿知识表示形式,它通过将各种矿床地质、矿物、矿体和勘探开发技术等要素以及要素之间的关系进行关联和表达,形成一个知识图谱结构,为矿产资源的预测和评估提供了新的途径。本研究从数据源和文献中收集了钦杭成矿带斑岩铜矿的相关知识,利用自然语言处理技术构建了知识图谱并和机器学习技术进行知识的自动化提取和推理。本研究通过构建找矿知识图谱模型,对钦杭成矿带斑岩铜矿的实体、属性和关系进行了表达和存储。在此基础上,利用自然语言处理技术对知识图谱进行了语义关联和推理,实现了知识的自动化提取和推理。本研究还建立了查询问答和可视化系统,使用户可以通过查询实体、属性或关系来获取相关信息,并以可视化的方式展示钦杭成矿带斑岩铜矿信息的结构和关联。最后,本研究通过实验和测试验证了基于知识图谱的智能应用在钦杭成矿带斑岩铜矿找矿工作中的有效性和准确性。与传统方法相比,该应用能够在短时间内提供更全面、准确的找矿建议。未来,将深化图算法应用和推荐系统,以满足不同场景下的找矿需求,并拓展其在其他相关领域的应用潜力。

关键词: 知识图谱, 矿床本体, 斑岩铜矿, 钦杭成矿带, 成矿预测

Abstract:

The mineral exploration knowledge graph (MEKG) is a component of Earth system-mineralization system-mining system correlation graphs, which represents an intersection between Earth science and data science and provides a novel approach for the prediction and evaluation of mineral resources. The conventional mineral exploration methods suffer from information asymmetry/inefficiency/inaccuracy, thus have limitations in effective utilization of geological data. To address this issue, we collect knowledge data relating to porphyry copper ore in the Qin-Hang mineralization belt using both primary and literature data sources, and construct a MEKG with automated knowledge extraction and reasoning using natural language processing (NLP) techniques. Briefly, the MEKG model represents the entities and attributes of porphyry copper ore and their relationships in the Qin-Hang mineralization belt; based on this framework, NLP techniques are used to semantically correlate and reason over the knowledge graph, enabling automated knowledge extraction and reasoning. In addition, we develop a Q&A and visualization system that allows users to query entities/attributes and their relationships to obtain relevant information and visualize the data structure and data relationship. This study demonstrates the effectiveness and accuracy of knowledge-based intelligent applications in porphyry copper ore exploration in the Qin-Hang belt through experimentation and testing. Compared with traditional methods, this application provides more comprehensive and accurate mining suggestions in a short time to aid geological exploration decision-making. Also this study can serve as reference for other mineral exploration fields. In the future, we aim to further enhance the performance and functionality of this knowledge model by broadening the graph algorithm applications and recommendation systems, so as to meet the needs of mineral explorations under different scenarios and expand the model’s application potential to other related fields.

Key words: knowledge graph, ore deposit ontology, porphyry copper deposit, Qin-Hang metallogenic belt, mineral prospecting mapping

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