A new deep neural network for phase picking with balanced speed and accuracy

The deep neural network （DNN） has achieved good results in phase picking. As a high complexity machine learning model, DNN suffers from high computational cost to achieve high accuracy. The experimental results show that there is no need to build a high complexity model for phase picking. So, we designed four network models with different complexity to improve speed and accuracy of phase picking based on characteristics of seismic waveforms, which offers a choice of accuracy and/or speed of the phase picking. And then we compared our results with those obtained from four existing DNN models, and verified the relative high speed and accuracy. More importantly, the DNN models can be compressed to within 2.0 MB after a variety of model compression methods, which allows the structure to perform high-speed phase picking with relatively high accuracy on low-power-consumption devices.