Deep Learning for Picking Seismic Arrival Times

Arrival times of seismic phases contribute substantially to the study of the inner working of the Earth. Despite great advances in seismic data collection, the usage of seismic arrival times is still insufficient because of the overload manual picking tasks for human experts. In this work we employ a deep-learning method (PickNet) to automatically pick much more P and S wave arrival times of local earthquakes with a picking accuracy close to that by human experts, which can be used directly to determine seismic tomography. A large number of high-quality seismic arrival times obtained with the deep-learning model may contribute greatly to improve our understanding of the Earth's interior structure. Plain Language Summary Deep learning is currently attracting immense research interest in seismology due to its powerful ability to deal with huge seismic data collections. In this study we developed a deep-learning model (PickNet) that can rapidly pick a great number of first P and S wave arrival times precisely from local earthquake seismograms. The picking accuracy of the arrival times provided by our PickNet model is close to that by human experts. The data are good enough to be used directly to determine high-resolution 3-D velocity models of the Earth. Our PickNet model can deal with seismic waveforms provided by data centers of different earthquake networks. Furthermore, our PickNet model is also a potential tool for automatically picking later seismic phases accurately. A large number of high-quality seismic arrival times can be used to illuminate the Earth structure clearly. Hence, this study may greatly contribute to improve our knowledge of the Earth's interior.