Seismic phase picking of coal mine microseismic data based on lightweight CNN

Abstract

In coal mining, the high-noise underground environment poses significant challenges to the accuracy and stability of microseismic (MS) signal first-arrival picking. This study proposes a high-robustness automatic phase picking method based on a lightweight Convolutional Neural Network (CNN). The method uses a multi-layer convolution and adaptive pooling structure to efficiently extract waveform features across temporal scales, and introduces a regression prediction strategy to directly output the first-arrival time, eliminating the quantization error induced by classification-based methods. The model was validated on 1,791 manually labelled single-component records of 597 microseismic events from the 6305 working face of Xinjulong Coal Mine, Shandong Province. On the test set, the proportion of first-arrival errors not exceeding 100 ms reached 80.27%. Compared with the traditional STA/LTA method, the overall relative error was significantly reduced, with the average relative error decreasing by approximately 50 ms. The model maintained high picking accuracy and stability under high-noise and complex geological conditions, and under high-SNR conditions it effectively reduced the subjective bias of manual picking. The findings provide reliable technical support for high-precision localization of microseismic events and the early warning of dynamic hazards in coal mines.