基于微震监测的地下矿山开采扰动动力灾害分析与预测研究

发布时间：2018-05-01 19:38

本文选题：开采扰动 + 动力灾害　；参考：《武汉科技大学》2015年硕士论文

【摘要】：地下矿山的采矿作业正逐渐进入深部开采阶段，此时因开采扰动引起的动力灾害也慢慢增多，，其中包括岩爆和流变等高应力问题就非常突出，它是影响矿山安全生产非常重要的动力灾害表现形式，会导致井下作业人员的伤亡和财产损失进一步加大。首先，对地下矿山开采扰动动力灾害的典型表现形式和破坏机制进行了理论研究，比较了不同动力灾害监测方法的优缺点，选取了微震监测技术作为动力灾害监测的手段并说明了其工作的原理。然后，结合地下矿山实际开采作业的情况，在对不同系统比较的基础上，选择分布式与集中式相结合的微震监测系统，完成系统的总体设计方案；运用模块化和层次化思想对系统软件进行设计与开发，从而让微震监测系统能够进行实时在线的微震数据采集、处理和分析的功能。其次，运用小波包分析原理将微震监测系统采集到的微震信号进行一系列的波形分析、波形变换、波形综合、波形识别的加工处理，从中获取时频特征信息，就能对该事件震源类型进行区分和判断。最后，借助支持向量机的方法建立微震事件分类器模型和微震预测模型，不断优化微震事件的自动识别学习能力，为后期微震数据库的完善提供更多有效数据，通过该数据库进行微震事件的实时回归预测，为开采扰动动力灾害的实时预测提供科学依据。
[Abstract]:Mining operations in underground mines are gradually entering the stage of deep mining. At this time, the dynamic disasters caused by mining disturbances are gradually increasing, including the problems of high stress such as rock burst and rheology. It is a very important form of dynamic disaster affecting mine safety, which will cause casualties and property losses to further increase. Firstly, the typical manifestation and failure mechanism of disturbance dynamic disaster in underground mining are studied theoretically, and the advantages and disadvantages of different dynamic disaster monitoring methods are compared. The microseismic monitoring technology is selected as the means of dynamic disaster monitoring and the principle of its work is explained. Then, combined with the actual mining operation of underground mines, on the basis of comparison of different systems, the microseismic monitoring system combined with distributed and centralized is selected to complete the overall design of the system. The system software is designed and developed by modularization and hierarchy, so that the microseismic monitoring system can collect, process and analyze the microseismic data in real time and online. Secondly, a series of waveform analysis, waveform transformation, waveform synthesis, waveform recognition processing are carried out by using wavelet packet analysis principle to obtain time-frequency characteristic information from the microseismic signal collected by the microseismic monitoring system. The source type of the event can be distinguished and judged. Finally, the support vector machine (SVM) method is used to establish the microseismic event classifier model and the microseismic prediction model, and to continuously optimize the learning ability of automatic recognition of microseismic events, so as to provide more effective data for the improvement of microseismic database in the later period. The real-time regression prediction of microseismic events can provide scientific basis for real-time prediction of mining disturbance dynamic disasters.
【学位授予单位】：武汉科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TD326

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