基于微震监测的地下矿山地质灾害分析预测

发布时间：2018-05-08 18:21

本文选题：微震监测系统 + 矿山地质灾害　；参考：《武汉科技大学》2013年硕士论文

【摘要】：随着地下矿山开采深度日益增加，开采难度也逐渐加大，再加上矿山特殊的作业环境、复杂的地质情况，导致了各种各样的灾害事故，如塌陷、泥石流、冒顶、片帮、突水等，给矿山企业及人们的生命财产安全带来了严重的威胁。利用微震监测技术的原理，，针对金山店铁矿的实际开采情况，将整套实时在线微震监测系统布置在西区-340m水平，横纵方向共布置了16通道的传感器，通过Labview软件的强大图形化编程能力，实现微震动信号的采集、信号储存、信号读取、信号分析处理等功能。通过对采集信号的经验分析直观的得出波形的幅值、持续时间、间隔时间、形状分布等特征值，从中可以反映出爆破事件和小能量事件对岩体的破坏程度不会引起灾害。通过波形的频谱分析得出信号的瞬时频率、能量比值，及频率出现的最大可能值为100HZ和150HZ。最后，将两种方法结合总结出波形特征值，再将支持向量机方法与经验模态分解法相结合，预测出波形特征值的发展趋势，得出此阶段此区域发生顶板冒落、坍塌、泥石流事故的可能性很小。此外，由于此套微震监测系统在金山店铁矿投入运行的时间短暂，还需要更长期的现场监测数据，以便将来更加准确的分析预测矿山地质灾害。
[Abstract]:With the increasing mining depth of underground mines, the difficulty of mining has gradually increased. In addition, the special working environment and complex geological conditions of the mines have led to various disasters and accidents, such as collapse, debris flow, roof fall, cover, water inrush and so on. It has brought serious threat to the safety of mine enterprises and people's life and property. Based on the principle of microseismic monitoring technology and in view of the actual mining situation of Jinshandian Iron Mine, the whole set of real-time on-line microseismic monitoring system is arranged at the level of -340 m in the western region, and 16 channels of sensors are arranged in the horizontal and longitudinal direction. Through the powerful graphical programming ability of Labview software, the functions of signal acquisition, signal storage, signal reading, signal analysis and processing are realized. The characteristic values such as amplitude, duration, interval time, shape distribution and so on are obtained from the empirical analysis of the collected signals, which can reflect that the damage degree of rock mass caused by blasting events and small energy events will not cause disaster. The maximum possible values of the instantaneous frequency, the ratio of energy to the frequency and the frequency of the signal are 100HZ and 150HZ by the spectrum analysis of the waveform. Finally, the two methods are combined to sum up the waveform eigenvalues, and then the support vector machine (SVM) method is combined with the empirical mode decomposition method to predict the development trend of the waveform eigenvalues, and it is concluded that the roof caving and collapse occur in this area at this stage. The probability of debris flow accident is very small. In addition, since the microseismic monitoring system was put into operation in Jinshandian Iron Mine for a short time, more long-term monitoring data are needed for more accurate analysis and prediction of mine geological hazards in the future.
【学位授予单位】：武汉科技大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TD76

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