摘要
为提高复杂地质条件下危岩失稳崩塌灾害的监测预警水平与防治效率,通过广泛检索和深入分析国内外相关文献,系统研究了危岩体地质灾害,危岩体监测预警技术的研究现状、存在问题及发展趋势。结果表明,危岩监测预警技术已由传统人工观测逐步发展为基于三维激光扫描、光纤传感等的实时传感监测,并融合机器学习(machine learning,ML)与深度学习(deep learning,DL)实现智能化判别;微震监测与多物理场耦合模拟成为重要补充。然而,当前技术仍存在孕灾特征识别精度不足、失稳前兆动态判据缺失、预警时效性受限等瓶颈。未来研究可聚焦多源数据融合,加强机理驱动与数据驱动的融合建模,构建高效智能化预警与应急响应体系,并推动危岩灾害防治从被动治理向主动防控转变。
To improve the monitoring and early warning levels and prevention and control efficiency of instability and collapse disasters of unstable rock masses under complex geological conditions,through extensive retrieval and in-depth analysis of relevant domestic and international literature,a systematic study was conducted on the geological disasters of unstable rock masses,as well as the current research status,existing problems,and development trends of monitoring and early warning technologies for unstable rock masses.The results indicated that rockfall monitoring and early warning technologies had evolved from traditional manual observation to real-time sensor-based monitoring using three-dimensional laser scanning and fiber optic sensing,and further integrated machine learning(ML)and deep learning(DL)for intelligent identification.Microseismic monitoring and multi-physical field coupling simulation had also become important supplements.However,current technologies still suffered from limited accuracy in identifying disaster-breeding features,the absence of dynamic criteria for instability precursors,and restricted timeliness of early warnings.Future research could focus on multi-source data fusion and the integration of mechanism-driven and data-driven modeling to establish an efficient and intelligent early warning and emergency response system,promoting a shift in rockfall disaster prevention from passive management to active control.
作者
孙先波
董珂雯
彭庞迪
金浩宇
黄勇
易金桥
胡涛
朱黎
宋健
SUN Xianbo;DONG Kewen;PENG Pangdi;JIN Haoyu;HUANG Yong;YI Jinqiao;HU Tao;ZHU Li;SONG Jian(College of Intelligent Systems Science and Engineering,Hubei Minzu University,Enshi 445000,China;The Second Geological Brigade of Hubei Geological Bureau,Enshi 445000,China)
出处
《湖北民族大学学报(自然科学版)》
2025年第4期580-584,600,共6页
Journal of Hubei Minzu University:Natural Science Edition
基金
湖北省重点研发计划项目(2022BAA060)。
关键词
危岩体
地质灾害
监测预警技术
机器学习
深度学习
unstable rock mass
geological disasters
monitoring and early warning technology
machine learning
deep learning
