Abstract: Aiming at the problems of the difficulty of identification casused by a  large number of noise interference in microseismic signals, we proposed a deep bidirectional gated recurrent unit recurrent neural network model and applied to microseismic data denoising. Firstly, we constructed a multi-layer bidirectional gated recurrent unit recurrent neural network model, and designed the network structure and training algorithm of the model. Secondly,  the validity of the model was verified by using Ricker wavelet forward modeling microseismic data, and  the proposed method was compared with the other four methods. Finally, by inputting the real microseismic data with noise into the trained model, the microseismic data without noise could be obtained. The simulation results show that the peak signal-to-noise ratio of the signal after denoising by the proposed method is about 36 dB higher than that before denoising, and the correlation coefficient value between the signals increases from 0.088 6 to 0.933 5. The practical application results also show that the proposed method can effectively reduce the noise in the actual microseismic data.

Key words: microseismic, data denoising, recurrent neural network, gated recurrent unit, deep learning