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A study of microseismic first arrival pickup based on image semantic segmentation

Microseismic detection is an important part of geological exploration, and accurate pickup of microseismic first arrival is the basis of microseismic monitoring events. In practical engineering applications, the microseismic monitoring system is located in a complex environment, the signal-to-noise ratio of microseismic signals is low, the manual pickup is time-consuming and labor-intensive, the traditional automatic pickup method cannot balance the pickup effect and efficiency and cannot adapt to noise intensity changes and other problems. To solve the above problems, in order to better understand the properties and characteristics of microseismic signals and improve the rate and accuracy of microseismic first arrival pickup, this paper proposes a microseismic first arrival pickup method based on image semantic segmentation, and uses actual microseismic monitoring data collected from mines for comparison experiments. The experimental results show that the method has high accuracy in locating microseismic events in complex datasets containing multiple microseismic types, and the average pickup error and stability of microseismic first arrival are improved compared with the other network first arrival pickup method, which can improve the efficiency of microseismic monitoring and has practical engineering application value.