A mine microseismic signal identification method based on energy distribution characteristics

Abstract

The invention discloses a mine microseismic signal identification method based on energy distribution characteristics, belonging to the field of signal analysis and identification, comprising the following steps: reading the microseismic signal x(t) to be identified; performing VMD decomposition on x(t) to obtain K Variational modal components arranged in order of frequency from high to low; calculate the frequency band energy of each modal component, and extract the energy percentage value of each modal component in the original signal to form an energy distribution vector P; take the energy distribution vector P as Calculate the center of gravity coefficient cx of the X-axis of the energy distribution based on the basis; identify the microseismic signal of the mine according to the identification threshold T, if cx>T is the microseismic signal of the coal rock mass rupture in the mine, and if cx≤T is the blasting vibration signal; finally the value of the identification threshold T Adaptive update. The invention can effectively distinguish the microseismic signal of coal rock mass rupture and the blasting vibration signal, and has the characteristics of strong adaptability, high accuracy and the like.

Description

technical field [0001] The invention belongs to the field of signal analysis and identification, in particular to a mine microseismic signal identification method based on energy distribution characteristics. Background technique [0002] Microseismic monitoring is an advanced and effective coal-rock dynamic disaster monitoring method developed in recent years. It can monitor the microseismic activities of coal and rock mass in real time, continuously and online, and form microseismic monitoring data. Due to the complex mine environment, there are a large number of interference signals such as background noise and blasting vibration on site, which makes the microseismic monitoring system unable to accurately identify and record effective microseismic events. In the later stage, it is necessary to rely on technicians to manually identify effective microseismic events, which seriously affects the identification of the microseismic monitoring system. efficiency. Due to the fre...

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Problems solved by technology

Due to the complex mine environment, there are a large number of interference signals such as background noise and blasting vibration on site, which makes the microseismic monitoring system unable to accurately identify and record effective microseismic events. In the later stage, it is necessary to rely on technicians to manually identify effective microseismic events, which seriously affects the identification of the microseismic monitoring system. efficiency

Due to the frequent occurrence of blasting operations in coal mines, and the microseismic and blasting vibration waveforms of coal and rock masses are very similar, manual identification methods often cause mishandling and identification is difficult

[0003] At present, the commonly used time-frequency analysis methods for mine microseismic signal waveform identification include Fourier transform, wavelet transform, wavelet packet transform, frequency slice wavelet transform and EMD, etc. The traditional Fourier transform is mainly used to analyze periodic stationary signals. The randomness and non-stationary microseismic signal analysis effect of mutation is not good; wavelet analysis can carry out time-frequency analysis at the same time, but it needs to choose the appropriate wavelet base to achieve better decomposition effect; EMD can deal with random non-stationary signal better, However, there are boundary effects and mode mixing in the EMD method, which leads to instability and non-uniqueness of EMD.

These methods have a certain degree of disadvantages when used in signal analysis, which increases the difficulty of signal identification and has a high misjudgment rate.

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