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

EPick: Multi-Class Attention-based U-shaped Neural Network for Earthquake Detection and Seismic Phase Picking