地学前缘 ›› 2024, Vol. 31 ›› Issue (4): 73-86.DOI: 10.13745/j.esf.sf.2024.5.10

• 大数据算法与图形大数据 • 上一篇    下一篇

基于CiteSpace的地质大数据与人工智能研究热点及前沿分析

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

  1. 1.中山大学 地球环境与地球资源研究中心, 广东 广州 510275
    2.中山大学 地球科学与工程学院, 广东 珠海 519085
    3.广东省地质过程与矿产资源探查重点实验室, 广东 珠海 519085
    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:2023-09-05 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: yupengp@mail2.sysu.edu.cn
  • 作者简介:朱彪彪(1996—),男,硕士研究生,研究方向:地质大数据挖掘与智能找矿。E-mail: zhubb8@mail2.sysu.edu.cn
  • 基金资助:
    国家重点研发计划项目(2022YFF0801201);广东省重点领域研发计划项目(2020B1111370001);国家自然科学基金联合基金项目(U1911202);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)

Research hotspots and cutting-edge analysis of geological big data and artificial intelligence based on CiteSpace

ZHU Biaobiao1,2,3(), CAO Wei1,2,3, YU Pengpeng1,2,3,*(), ZHANG Qianlong1,2,3, GUO Lanxuan4, YUAN Guiqiang4, HAN Feng1,2,3, WANG Hanyu1,2,3, ZHOU Yongzhang1,2,3   

  1. 1. Research Center for Global Environment and Earth Resources, Sun Yat-sen University, Guangzhou 510275, China
    2. School of Earth Sciences and Engineering, Sun Yat-sen University, Zhuhai 519085,China
    3. Guangdong Provincial Key Laboratory of Geological Process and Mineral Resources Exploration, Zhuhai 519085, China
    4. Shenzhen Zhongjin Lingnan Nonferrous Metals Co., Ltd., Shenzhen 518000, China
  • Received:2023-09-05 Revised:2024-02-21 Online:2024-07-25 Published:2024-07-10

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摘要：

为研究地质学领域的大数据和人工智能研究现状、热点和前沿,在中国知网(CNKI)核心期刊和Web of Science(WoS)核心数据库收集了2000—2022年相关中文文献3 600篇、英文文献1 803篇,利用社区结构分析软件CiteSpace,从合作作者、研究国家、研究机构、关键词聚类、关键词时空分布图谱等进行可视化分析,并统计了2021—2022年间,地质学领域国际顶级期刊(综合影响因子10以上)的文献进行前沿分析。分析结果表明,近10年内该研究领域全球累计发文量激增,以中国为代表的亚洲国家和以美国为代表的欧美国家研究为主,双方累计发文量相差不大,论文中介中心性欧美国家普遍较高。我国研究机构之间的交流合作居多,与国外的研究机构交流合作较少,国外研究机构则与之相反。该领域以应用机器学习类方法、知识图谱构建等,在地质灾害防治、地震解释、石油与天然气勘查、固体矿产资源预测等方向进行的科学研究为研究热点,以深度学习、集成学习、智能平台搭建等为手段的地球演化过程中的重大地质事件研究、全球性气候变化、极地及海洋地质研究、数字地质建模及定量分析、地震预报、地灾易发性精准评估等为研究前沿。

关键词: 地质大数据, 人工智能, 知识图谱, CiteSpace, 社区发现, 可视化

Abstract:

To investigate the current status, hotspots, and frontiers of big data and artificial intelligence research in the field of geology, this study conducts literature screening of relevant research publications between 2000-2022 using China National Knowledge Infrastructure (CNKI) and Web of Science (WoS) core databases. A total of 3600 Chinese and 1803 English articles are collected, and community structure analysis software CiteSpace is used for visual analysis of cooperation authors, research countries/institutions, keyword clustering, and keyword spatiotemporal distribution maps. Furthermore, a stochastic frontier analysis correction is conducted on publications by international top-tier geoscience journals (comprehensive impact factor ≥10) between 2021-2022. The global cumulative publication volume in this research field had surged in the past decade, led by Asian countries represented by China and European/American countries represented by the United States, with China and the United States showing no significant differences, and the betweenness centrality measures generally higher for European/American countries than for Asian countries. In China, research collaborations were mostly among domestic institutions and relatively rare with foreign research institutions, whilst the opposite was true in foreign countries. The research hotspots in this field were geological disaster prevention and control, earthquake interpretation, petroleum and natural gas exploration, and solid mineral resource prediction using machine learning and knowledge graphs. Research frontiers included significant geological events during Earth’s evolution, global climate change, polar and marine geology, digital geological modeling and quantitative analysis, earthquake prediction, and accurate assessment of geological disaster susceptibility by means of deep learning, integrated learning, and intelligent platform.

Key words: geological big data, artificial intelligence, knowledge graph, CiteSpace, community discovery, visualization

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