  Earthquake early warning systems are required to report earthquake locations
and magnitudes as quickly as possible before the damaging S wave arrival to
mitigate seismic hazards. Deep learning techniques provide potential for
extracting earthquake source information from full seismic waveforms instead of
seismic phase picks. We developed a novel deep learning earthquake early
warning system that utilizes fully convolutional networks to simultaneously
detect earthquakes and estimate their source parameters from continuous seismic
waveform streams. The system determines earthquake location and magnitude as
soon as one station receives earthquake signals and evolutionarily improves the
solutions by receiving continuous data. We apply the system to the 2016 Mw 6.0
earthquake in Central Apennines, Italy and its subsequent sequence. Earthquake
locations and magnitudes can be reliably determined as early as four seconds
after the earliest P phase, with mean error ranges of 6.8-3.7 km and 0.31-0.23,
respectively.
