机器学习在隧道岩爆微震监测预警中的应用

Application Status of Machine Learning in Microseismic Monitoring and Early Warning of Rockburst

深部隧道岩爆是地下工程建设中的安全隐患,准确预警岩爆能够保障工程人员的生命财产安全。机器学习等智能技术的出现为岩爆预警带来了新的思路和方法,提高了预警的准确性、及时性和智能化水平。对机器学习在深部隧道岩爆微震监测预警中的应用现状开展了系统研究。首先,对岩爆微震监测评估预警中的机器学习算法进行总结,分析了现有各类机器学习算法的特征优势,然后,对微震监测岩爆预警指标体系进行了归纳,进一步,分析了基于不同机器学习的岩爆微震监测预警应用效果。结果表明神经网络(NN)是岩爆预警算法中的热门方法,微震事件数(N)、微震能量(E)、视体积(V)及其变体是使用频次最高的岩爆预警指标,大部分岩爆预警指标个数主要在3~7个之间。岩爆等级是岩爆预警的研究热点,大部分机器学习方法的预警准确率基本能达到80%及以上,表明机器学习方法具有较好的应用效果与发展前景。最后,对发展方向进行了展望,更先进的机器学习算法、预警指标体系的有效性与全面性、样本的丰富性、岩爆发生时间预警、数据处理能力等需要进一步深入研究。

Rockburst in deep tunnels is a hazard during the underground engineering construction.Accurate early warning of rockburst can protect the lives and properties of engineering personnel.The intelligent technologies such as machine learning(ML)have brought new ideas and methods for rockburst early warning,which has improved the accuracy,timeliness and intelligence for early warning of rockburst.A systematic study on the current application of ML in microseismic(MS)monitoring and early warning of rockbursts in deep tunnels was carried out.Firstly,ML algorithms in the MS monitoring,evaluation and early warning of rockbursts were summarized.The characteristic advantages of the various types of ML algorithms were analyzed.Then,the indicator system for MS monitoring and early warning of rockburst was discussed.The applications of MS monitoring and early warning of rockburst based on different ML methods and their effects were analyzed.The results show that neural network(NN)is one of the most popular algorithms for rockburst warning,the MS event(N),MS energy(E),MS apparent volume(V)and its variants are the most frequently used MS parameters,and most of the rockburst warning parameters are between 3~7 in number.Rockburst intensity is the research hotspot of rockburst warning,and the warning accuracy based on most ML methods can reach 80%,which indicates that the ML method has good application effects and development prospects.Finally,prospects were made for the development direction of ML in MS monitoring and early warning for the rockburst in deep tunnels,i.e.advanced ML algorithms,the accuracy and comprehensiveness of the early warning indicator system,the richness of the sample,the time warning of the rockburst occurrence,and the capability of data processing to be further investigated in depth.