  Reliable earthquake detection and seismic phase classification is often
challenging especially in the circumstances of low magnitude events or poor
signal-to-noise ratio. With improved seismometers and better global coverage, a
sharp increase in the volume of recorded seismic data is witnessed. This makes
the handling of the seismic data rather daunting based on traditional
approaches and therefore fuels the need for a more robust and reliable method.
In this study, we investigate two deep learningbased models, termed 1D
ResidualNeuralNetwork (ResNet) and multi-branch ResNet, for tackling the
problem of seismic signal detection and phase identification, especially the
later can be used in the case where multiple classes is organized in the
hierarchical format. These methods are trained and tested on the dataset of the
Southern California Seismic Network. Results demonstrate that the proposed
methods can achieve robust performance for the detection of seismic signals,
and the identification of seismic phases, even when the seismic events are of
small magnitude and are masked by noise. Compared with previously proposed deep
learning methods, the introduced frameworks achieve 4% improvement in
earthquake monitoring, and a slight enhancement in seismic phase
classification.
