Research on automatic picking method of microseismic signal P wave based on deep learning mode

High-precision pick-up of P-wave signals from mine microseismic signals is an important prerequisite for precise locationof mine microseismic signals. Based on the time-domain characteristics of microseismic P-wave signals and the deep learning algorithm in the field of computer vision, this paper propose a picking DMSP method suitable for microseismic P-wave signals and constructs a suitable loss function. This method first builds an adaptive DAA-MINE denoises the microseismic signal in the mine, and then builds a segmentation joint picking Cut-SPto pick up the initial value and end point of the microseismic signal P wave. 3835 groups and 959 groups of mine microseismic signal data are used as training set and test set, respectively, to train and test the model proposed in this paper. The results show that:after the DAA-MINE model denoising, the average signal-to-noise ratio is improved and more energy is retained;compared with the ER algorithm, the MER algorithm, the WFM algorithm, and the PAT-S/K algorithm, the Cut-SP model The average picking error is low, the robustness is strong, and the recognition speed is faster, and it can meets the engineering needs. The pickup model constructed this time realizes the integration of deep learning neural network and mine microseismic monitoring, and provides a new method for automatically picking up data of microseismic data in intelligent mining. © 2021, Science Press. All right reserved.