考虑非平稳、强噪声信号到时差拾取的硬岩矿山微震定位方法研究

Rockburst and other ground pressure disasters in deep hard rock mining are highly destructive, caused by complex mechanism, and difficult to prevent and control, which can easily cause a large number of casualties and significant property losses. Microseismic monitoring and surrounding rock support are the most popular technologies to prevent and control the ground pressure disasters around the world. However, the coexistence of rock mass, goaf and backfilling in hard rock deep mines makes the monitoring environment complex and changeable. More background noise and interference signals are often recorded, which makes it more complex and difficult to pick up the time difference of arrival (TDOA) in microseismic source location. Therefore, how to automatically and accurately pick up the TDOA of the microseismic signals in the complex environment of deep mines becomes a challenging problem..This project intends to perform multi-scale correlation analysis on the wavelet spectra of two non-stationary signals by using cross-wavelet. The features of the cross-wavelet spectra of two signals are automatically extracted by the convolutional neural network (CNN). The microseismic source location method in hard rock mines by picking up the TDOA of non-stationary microseismic signals with strong noise is proposed on this basis. Then the characteristics of time, space and intensity of microseismic events are revealed by analyzing the monitoring data. The dynamic change law of dangerous area in rockmass with mining process is explored. A disaster identification and risk assessment system is developed to achieve real-time warning of strong seism in stope and guide the support design of surrounding rock, thus providing scientific guidance and technical guarantee for safe and efficient production of mines.

硬岩深部开采中岩爆等地压灾害破坏性强、致灾机理复杂、防控难度大，极易造成大量人员伤亡和财产损失。国际上防控地压灾害的主要手段是微震监测及围岩支护。但矿山开采走向深部以后硬岩矿山多中段、多采场开采中岩体、空区、充填体共存等情况使得监测环境更为复杂多变，背景噪音和干扰信号较多，导致微震定位到时差的拾取更为复杂和困难，所以如何在深部矿山复杂环境下自动精确拾取微震信号的到时差成为新的科学问题。.本项目拟通过交叉小波对两个非平稳信号的小波谱做多尺度的相关分析，使用卷积神经网络对两个同源信号的交叉小波谱图进行自动特征提取，在此基础上提出考虑非平稳、强噪声信号到时差拾取的硬岩矿山微震定位方法。根据监测数据分析微震事件的时间、空间、强度特征，探索矿山开采过程中岩体危险区域的动态变化规律，构建灾害识别与风险评估系统，实现采场强震实时预警并指导围岩精准支护，为矿山安全高效生产提供科学指导和技术保障。

本项目针对硬岩深部开采中岩爆等地压灾害破坏性强、致灾机理复杂、防控难度大等问题，以深部资源开采诱发岩体动力灾害为背景，通过理论分析、数值计算、室内试验与现场监测等手段，和信号时频分析、多尺度分析和现场环境特征自适应等技术，提出了考虑非平稳、强噪声信号到时差拾取的硬岩矿山微震定位方法，实现了时频域相关度特征的智能挖掘，建立了微震同源信号相关度评价准则，提高了单震源定位精度，实现了非稳定强噪声环境下的多震源定位。基于提出的算法和构建的硬岩矿山微震监测系统，监测并解析了典型矿山采场近区微震事件及岩爆灾害的“时-空-强”特征，获得了矿山开采岩体动力破坏的扰动特征和破坏区域，揭示了深部硬岩矿山采动围岩能量的聚集、迁移及释放规律，形成了硬岩矿山动力灾害精准实时预警和综合防控技术。项目研究成果在贵州开磷、山东黄金等矿业集团推广应用，实时精准监测了深部资源开采区域应力、能量、微震变化情况，构建了基于微震监测信息和动力灾害失稳判据的深部硬岩动力灾害预警系统，保障了这类年产百万吨矿山的安全高效生产。.基于以上研究内容，以第一或通讯作者在Eng Geol, Fuel, Tunn Undergr Sp Tech, Rock Mech Rock Eng等行业期刊上发表SCI论文18篇（其中中科院1区10篇、JCR1区16篇，该基金号第一标注8篇、第二标注9篇），出版学术专著《硬岩矿山微震定位理论与方法》1部，获得发明专利5项，主持和参与制订国家行业标准3部。“硬岩矿山开采震源智能感知与安全保障关键技术”获中国职业安全健康协会科技进步一等奖(本人排名第2)。“深部金矿整体地压管控与高效开采技术”获中国黄金协会科学技术一等奖(本人排名第1)。