兼顾速度和精度的深度神经网络震相拾取

深度神经网络虽然在震相拾取中取得了良好效果，但作为高复杂度的机器学习模型，深度神经网络在取得较高精度的同时需要付出较高的计算代价，而且试验研究表明震相拾取中并不需要过高的模型复杂度。为此，本文根据地震波形的特点设计了四种具有不同复杂度的深度神经网络改进模型，可以综合具体的精度和速度需求从中选取合适的模型。在此基础上，将改进模型与现有四种到时拾取的深度学习网络模型进行了对比，结果表明本文中的网络模型在到时拾取上具有较高的速度和精度。同时，本文的深度神经网络通过使用多种深度学习模型压缩手段可将震相拾取模型的大小压缩到2.0 MB以内，从而使得模型可以在低功耗设备上完成高速震相拾取的同时尽可能地减少精度损失。 

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.