Automatic picking method of microseismic first arrival based on support vector machine based on particle swarm optimization

Abstract

Automatic and accurate arrival time pickup of microseismic first-arrival waves is an important prerequisite for high precision microseismic source location. Aiming at the low efficiency of the traditional manual pickup method and the low accuracy of the long, short window energy ratio (STA/LTA) method commonly used in automatic pickup for low signal-to-noise ratio signals, an automatic picking method of microseismic first arrival based on support vector machine based on particle swarm optimization is proposed. Firstly, according to the amplitude and energy of microseismic signal and the energy ratio of adjacent time, the signals are marked with different categories. Then the parameters are optimized by particle swarm optimization algorithm to construct the support vector machine model of microseismic first-arrival. Finally, the data is substituted to extract the microseismic first-arrival. The experiment is carried out with the microseismic monitoring data of underground roadway in a gold mine. The experimental results show that, under the condition of low SIGNal-to-noise ratio, the picking accuracy of the proposed method is 96.4%, the average pickup error is 3.9ms, and the picking accuracy and accuracy are better than STA/LTA method.