Mine Ground Pressure Monitoring and Early Warning Based on Deep Learning Data Analysis

Abstract

In order to ensure the safe mining of kilometer mining working surface threatened by impact ground pressure, a metal mine ground pressure monitoring and early warning based on deep learning data analysis are proposed. This paper expounds the theoretical basis of rock burst, analyzes the inducing factors of deep well rock burst, analyzes and introduces the classification of rock burst, focuses on the progressive failure process of rock burst and standard of rock fracture depth of deep ore and rock in a metal mine, carries out triaxial stress-strain test on the core in the laboratory, and evaluates the tendency of rock burst for deep ore and rock through elastic strain generation, strength brittleness coefficient method, and deformation brittleness coefficient method. The real-time monitoring and early warning system of rock burst can monitor the dynamic change of advance stress in the working face in real time and give real-time early warning to the dangerous area and degree of rock burst. The experimental results show that the working face enters the fault affected area when it advances 170 m in front of the fault. When the working face advances to 100 m in front of the fault, it enters the high stress area formed by the superposition of fault tectonic stress and mining stress. When the working face advances to 40 m in front of the fault, the stress reaches the maximum. Therefore, the system can accurately predict the impact risk area and its risk degree and realize the safe mining of high impact risk face.