Seismic Phase Picking Algorithm Based on Improved Bi-LSTM

Abstract

In order to solve the problem of traditional waveform detection algorithms such as relying on manual setting of thresholds and low precision of phase picking, a seismic phase picking algorithm based on improved Bi-LSTM was proposed. This method uses filtering, normalization, and noise addition to preprocess the original waveform data, and uses the Bi-LSTM model to perform adaptive deep feature extraction of the seismic signal. Meanwhile, in order to solve the overfitting of the Bi-LSTM model, Spatial-Dropout mechanism is introduced to restrict the sparsity of the Bi-LSTM model in random area, and finally the Time-Distributed mechanism is introducted to automatically determine the arrival time of P-wave from the time domain dimension for the event-noise binary classification problem. Comparative experiments on relevant seismic data sets show that the precision and accuracy of the P-wave pickup of this model is 90%, 80%. Compared with traditional RNN network models such as LSTM and GRU, present algorithm gives a precision increased by 6% and 5% respectively. At the same time, the model does not need to manually set the threshold, and has strong robustness to the problem of P-wave pickup of complex waveform data.

Keywords

• deep learning
• bi-lstm
• seismic phase pickup
• recurrent neural network (rnn)
• machine learning