摘要
随着遥感、物联网、人工智能、大数据、云计算以及近年来迅速发展的大语言模型(large language models,简称LLMs)等高新技术持续取得突破,地震与地质灾害研究正加速从传统依赖单一数据源与经验规则的范式,迈向多源信息融合与智能驱动的风险识别和决策支持体系。基于“高新技术在地震与地质灾害领域的应用研究”专栏,系统梳理了当前在物理仿真模拟、深度学习识别、遥感集成分析、智能预警技术与知识图谱构建等关键方向的研究进展,概括展示了高新技术在灾害风险监测、致灾机制解析与应急响应支撑中的典型应用与发展趋势。在此基础上,进一步总结了多模态数据集成、灾害链建模、模型泛化能力与场景适应性等方面面临的技术瓶颈,探讨了大语言模型在地震与地质灾害领域中的潜在价值,包括知识抽取、因果推理与多场景风险研判等方面的前沿探索。
[Significance]With continuous advances in high technologies such as remote sensing,the Internet of Things,artificial intelligence,big data,cloud computing,and more recently,large language models(LLMs),the field of earthquake and geological disaster research is shifting from traditional paradigms relying on single data sources and empirical models toward integrated systems driven by multi-source data fusion and intelligent decision support.[Progress]This article,based on the themed column“Applications of Advanced Technologies in Earthquake and Geological Hazard Research,”reviews recent progress across five key directions:physical simulation modeling,deep learning-based recognition,remote sensing integration,intelligent early warning techniques,and knowledge graph construction.These studies collectively demonstrate how cutting-edge technologies are being applied to hazard monitoring,mechanism analysis,and emergency response.[Conclusions and Prospects]On this basis,the article further identifies current technical bottlenecks,including challenges in multimodal data integration,disaster chain modeling,model generalization,and scenario adaptability,and explores the potential role of LLMs in this field,particularly in knowledge extraction,causal inference,and multi-scenario risk assessment.
作者
许冲
高明星
薛智文
黄雨
吴礼舟
邬忠虎
XU Chong;GAO Mingxing;XUE Zhiwen;HUANG Yu;WU Lizhou;WU Zhonghu(School of Geology and Mining Engineering,Xinjiang University,Urumqi 830047,China;National Institute of Natural Hazards,Ministry of Emergency Management of China,Beijing 100085,China;Key Laboratory of Compound and Chained Natural Hazards Dynamics,Ministry of Emergency Management of China,Beijing 100085,China;College of Resources and Environment,Xingtai Hebei 054001,China;Xingtai Key Laboratory of Geo-Information and Remote Sensing Technology Application,Xingtai Hebei 054001,China;School of Emergency Management Science and Engineering,University of Chinese Academy of Sciences,Beijing 100049,China;Department of Geotechnical Engineering,College of Civil Engineering,Tongji University,Shanghai 200092,China;Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education,Tongji University,Shanghai 200092,China;Institute of Future Civil Technology,Chongqing Jiaotong University,Chongqing 400074,China;College of Civil Engineering,Guizhou University,Guiyang 550025,China)
出处
《地质科技通报》
北大核心
2025年第4期16-22,共7页
Bulletin of Geological Science and Technology
基金
国家重点研发计划项目“降雨型群发滑坡时空多尺度风险区划与韧性评价技术”(2024YFC3012604)
重庆市水利局项目“三峡库区消落区岩体劣化灾害监测预警技术方法研究”(CQS24C00836)。
关键词
地质灾害
高新技术
遥感与InSAR
深度学习与知识图谱
灾害模拟与智能预警
文献计量学
geological disaster
advanced technology
remote sensing and InSAR
deep learning and knowledge graph
disaster simulation and early warning
bibliometrics
