LPPN: A Lightweight Network for Fast Phase Picking
Ziye Yu, Weitao Wang
Abstract
Abstract We here present one lightweight phase picking network (LPPN) to pick P/S phases from continuous seismic recordings. It first classifies the phase type for a segment of waveform, and then performs regression to get accurate phase arrival time. The network is optimized using deep separable convolution to reduce the number of trainable parameters and improve its computation efficiency. Experiments using the STanford EArthquake Dataset (STEAD) show that the precision of LPPN can reach 95.2% and 83.7% with the recalls 94.4% and 84.7% for P and S phases, respectively. The classification–regression approach shows comparable performance to traditional point-to-point methods with lower computation cost. LPPN can be configured to have different model size and run on a wide range of devices. It is open-source and can support phase picking for large-scale dataset or in other speed sensitive scenarios.