Deep rock mass rock blasting forecasting and early warning method based on blast vibration monitoring

Abstract

A deep rock mass rock blasting forecasting and early warning method based on blast vibration monitoring comprises the following steps: setting a blast vibration sensor, collecting a signal and converting the signal into an electric signal for recording, amplifying the electric signal through a signal amplifier, forwarding the electric signal to a far-end signal analyzing and processing system through a wireless electric signal transponder, transmitting the received signal to a vibration signal identification and filter system of a computer, carrying out spectral analysis, recognition and separation on the vibration waveform, extracting characteristic parameters of a separated wave signal, and forecasting or evaluating disasters. The deep rock mass rock blasting forecasting and early warning method based on blast vibration monitoring has the advantages that the deep rock mass rock blasting is forecast and warned early through signal analysis via vibration monitoring equipment, easiness, convenience and practicability are achieved, great manpower and material resource input is not required, site construction is not disturbed, flexibility and convenience are achieved, and the deep rock mass rock blasting forecasting and early warning method is high in adaptability to different projects.

Description

technical field [0001] The invention relates to a method for predicting and early warning of rockburst in deep rock mass based on blasting vibration monitoring, which is suitable for strong rockburst (rockburst) during the excavation of deep underground caverns or deep mining in the fields of water conservancy and hydropower projects, transportation, mines, etc. predictions and early warnings. Background technique [0002] Because rockburst is a very complex phenomenon of dynamic geological disasters, there are many influencing factors, which also determine the diversity and complexity of rockburst prediction. Analyze the rockburst in terms of fractal and sudden change, and put forward various hypotheses and criteria. For example, the rockburst discrimination criterion of Rusesenes, Turchaninov, Jia Yuru, Wang Yuanhan et al.; the construction geological advance forecast method and ?? / ?R criterion prediction method of Xu Linsheng et al.; the energy storage test analysis and ...

AI Technical Summary

Problems solved by technology

For projects with a small investment scale, or large-length traffic and water diversion tunnels, etc., the working surface is small and changes rapidly with the construction progress. The traditional monitoring system layout is relatively difficult, and there are factors such as low accuracy and insufficient investment.

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