Coal mine microseismic identification and first-arrival picking based on Conv-LSTM-Unet

Microseismic identification and first-arrival picking are the fundamental parts of microseismic monitoring. A deep learning model of convolution long short-term memory network (Conv-LSTM-Unet) was proposed to reduce the manual task of microseismic identification and first-arrival picking and overcome the problems of complex parameter design, low accuracy, and poor stability of traditional methods. This paper integrates microseismic identification and first-arrival picking tasks into a semantic segmentation task by using Conv-LSTM-Unet. In order to learn the spatio-temporal characteristics of microseismic, the model is based on the Unet network, and the Conv-LSTM model is formed by combining the convolution operation with LSTM, which replaces the convolution part of the original Unet network. Meanwhile, the microseismic signal is divided into three forms of single component, time-frequency map, and three-component signal to study the effect of microseismic input form on the identification model and first-arrival picking. The results show that when the signal input is three-component form, the model recognition and first-arrival picking effect are best. The Conv-LSTM-Unet model has outperformed other traditional models in first-arrival picking, with recognition accuracy up to 96.55% and maximum error of 4 ms.