基于文献计量的国内地质大数据研究态势分析

    Situation analysis of domestic geological big data research based on bibliometrics

    • 摘要: 基于2012—2022年间CNKI数据库收录的相关论文,对我国地质大数据领域的研究进行科学计量分析,以期为有关管理部门和广大同行了解近十年来国内地质大数据研究态势提供参考。研究结果表明:地质大数据研究在2015年之后受到更为广泛的关注,目前处于稳定高产期。发文量最大、影响力最大的研究机构网络有三个,其核心分别是中国地质调查局发展研究中心、中国地质大学(北京)和中国地质大学(武汉)。作者共现分析显示,形成了一个大型作者群和四个小型作者群,但该领域的核心作者群尚未形成。研究热点主要有地质大数据存储与管理、地质大数据挖掘流程及算法研究、地质大数据应用。其中,以机器学习、深度学习等方法为代表的地质大数据挖掘方法研究热度最高、成果最多;地质大数据应用探索的热点领域主要包括智能地质调查、矿产资源预测、地球化学、地质灾害预警、资源智能开采。该领域研究热点经历了三个显著的发展阶段:①技术探索阶段(2013—2015年),主要关注大数据技术在地质灾害预警和矿产预测等领域的初步应用;②平台化与数据治理阶段(2016—2018年),研究重点转向地质资料的标准化整合与云平台建设,基于Hadoop、Spark等分布式计算技术构建地质大数据平台,促进数据共享与协同分析;③智能融合阶段(2019—2022年),研究进入以深度学习和地学知识图谱为核心的智能化发展阶段,形成了“数据-知识”双驱动的研究范式。当前研究前沿聚焦于多模态数据融合、混合建模、协同智能技术等,标志着地质大数据向智能化、知识化、协同化方向发展。

       

      Abstract: Based on the relevant papers collected in CNKI database from 2012 to 2022, this paper makes a scientometric analysis of the research in the field of geological big data in China, in order to provide reference for the relevant management departments and the general peers to understand the development trend and evolution of domestic geological big data research in the past decade. The results show that geological big data research has received more attention after 2015, and is currently in a stable and high-yield period. There are three research institution networks with the largest number of publications and the greatest influence, the core of which are the Development Research Center of China Geological Survey, China University of Geosciences(Beijing) and China University of Geosciences(Wuhan). Co-occurrence analysis shows that 1 large and 4 small groups of authors have been formed, but the core group of authors in this field has not yet been formed. The main research hotspots are geological big data storage and management, geological big data mining process and algorithm research, geological big data application. Among them, the geological big data mining methods represented by machine learning and deep learning have the highest research heat and the most achievements. The hot areas of geological big data application exploration mainly include intelligent geological survey, mineral resource prediction, geochemistry, geological disaster warning, and intelligent resource mining. The research hotspots in this field have undergone three distinct developmental phases: the technology exploration phase (2013-2015), primarily focusing on preliminary applications of big data technologies in geological hazard early warning and mineral resource prediction; the platformization and data governance phase (2016-2018), where research emphasis shifted toward standardized integration of geological data and cloud platform development, establishing geological big data platforms based on distributed computing technologies such as Hadoop and Spark to facilitate data sharing and collaborative analysis; and the intelligent integration phase (2019-2022), during which research advanced into an intelligent development stage centered on deep learning and geoscience knowledge graphs, forming a “data-knowledge” dual-driven research paradigm. The current research frontier focuses on multi-modal data fusion, hybrid modeling approaches, and collaborative intelligent technologies, marking a transformative trend in geospatial big data toward intelligence, knowledge-driven processing, and collaboration.

       

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