  Identifying the arrival times of seismic P-phases plays a significant role in
real-time seismic monitoring, which provides critical guidance for emergency
response activities. While considerable research has been conducted on this
topic, efficiently capturing the arrival times of seismic P-phases hidden
within intensively distributed and noisy seismic waves, such as those generated
by the aftershocks of destructive earthquakes, remains a real challenge since
most common existing methods in seismology rely on laborious expert
supervision. To this end, in this paper, we present a machine learning-enhanced
framework based on ensemble learning strategy, EL-Picker, for the automatic
identification of seismic P-phase arrivals on continuous and massive waveforms.
More specifically, EL-Picker consists of three modules, namely, Trigger,
Classifier, and Refiner, and an ensemble learning strategy is exploited to
integrate several machine learning classifiers. An evaluation of the
aftershocks following the MS 8.0 Wenchuan earthquake demonstrates that
EL-Picker can not only achieve the best identification performance but also
identify 120% more seismic P-phase arrivals as complementary data. Meanwhile,
experimental results also reveal both the applicability of different machine
learning models for waveforms collected from different seismic stations and the
regularities of seismic P-phase arrivals that might be neglected during manual
inspection. These findings clearly validate the effectiveness, efficiency,
flexibility and stability of EL-Picker.
