  The detection of earthquakes is a fundamental prerequisite for seismology and
contributes to various research areas, such as forecasting earthquakes and
understanding the crust/mantle structure. Recent advances in machine learning
technologies have enabled the automatic detection of earthquakes from waveform
data. In particular, various state-of-the-art deep-learning methods have been
applied to this endeavour. In this study, we proposed and tested a novel phase
detection method employing deep learning, which is based on a standard
convolutional neural network in a new framework. The novelty of the proposed
method is its separate explicit learning strategy for global and local
representations of waveforms, which enhances its robustness and flexibility.
Prior to modelling the proposed method, we identified local representations of
the waveform by the multiple clustering of waveforms, in which the data points
were optimally partitioned. Based on this result, we considered a global
representation and two local representations of the waveform. Subsequently,
different phase detection models were trained for each global and local
representation. For a new waveform, the overall phase probability was evaluated
as a product of the phase probabilities of each model. This additional
information on local representations makes the proposed method robust to noise,
which is demonstrated by its application to the test data. Furthermore, an
application to seismic swarm data demonstrated the robust performance of the
proposed method compared with those of other deep learning methods. Finally, in
an application to low-frequency earthquakes, we demonstrated the flexibility of
the proposed method, which is readily adaptable for the detection of
low-frequency earthquakes by retraining only a local model.
