Intelligent phase picking of microseismic signals based on ResUNet in underground engineering

Abstract

With the continuous expansion of deep underground engineering and the growing demand for safety monitoring, microseismic monitoring has become a core method for early warning of rock mass fracture and engineering stability assessment. To address problems in existing methods, such as low data processing efficiency and poor phase recognition accuracy under low signal-to-noise ratio (SNR) conditions in complex geological environments, this study proposes an intelligent phase picking model based on ResUNet. The model integrates the residual learning mechanism of ResNet with the multi-scale feature extraction capability of UNet, effectively mitigating the vanishing gradient problem in deep networks. It also achieves cross-layer fusion of shallow detail features and deep semantic features through skip connections in the encoder-decoder structure. Compared with traditional short-time average/long-time average (STA/LTA) algorithms and advanced neural network models such as PhaseNet and EQTransformer, ResUNet shows superior performance in picking P- and S-wave phases. The model was trained on 400000 labeled microseismic signals from the Stanford earthquake dataset (STEAD) and was successfully applied to the Shizhuyuan polymetallic mine in Hunan Province, China. The results demonstrate that ResUNet achieves high picking accuracy and robustness in complex geological conditions, offering reliable technical support for early warning of disasters such as rockburst in deep underground engineering.

Keywords

underground engineering / microseismic monitoring / phase picking / deep learning / ResUNet architecture / rock fracture early warning

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