  Earthquake detection and seismic phase picking not only play a crucial role
in travel time estimation of body waves(P and S waves) but also in the
localisation of the epicenter of the corresponding event. Generally, manual
phase picking is a trustworthy and the optimum method to determine the phase
arrival time, however, its capacity is restricted by available resources and
time. Moreover, noisy seismic data renders an additional critical challenge for
fast and accurate phase picking. In this study, a deep learning based model,
EPick, is proposed which benefits both from U shaped neural network (also
called UNet)and attention mechanism, as a strong alternative for seismic event
detection and phase picking. On one hand, the utilization of UNet structure
enables addressing different levels of deep features. On the other hand,
attention mechanism promotes the decoder in the UNet structure to focus on the
efficient exploitation of the low-resolution features learned from the encoder
part to achieve precise phase picking. Extensive experimental results
demonstrate that EPick achieves better performance over the benchmark method,
and show the models robustness when tested on a different seismic dataset.
