  Shortage of labeled seismic field data poses a significant challenge for
deep-learning related applications in seismology. One approach to mitigate this
issue is to use synthetic waveforms as a complement to field data. However,
traditional physics-driven methods for synthesizing data are computationally
expensive and often fail to capture complex features in real seismic waveforms.
In this study, we develop a deep-learning-based generative model, PhaseGen, for
synthesizing realistic seismic waveforms dictated by provided P- and S-wave
arrival labels. Contrary to previous generative models which require a large
amount of data for training, the proposed model can be trained with only 100
seismic events recorded by a single seismic station. The fidelity, diversity
and alignment for waveforms synthesized by PhaseGen with diverse P- and S-wave
arrival labels are quantitatively evaluated. Also, PhaseGen is used to augment
a labelled seismic dataset used for training a deep neural network for the
phase picking task, and it is found that the picking capability trained with
the augmented dataset is unambiguously improved. It is expected that PhaseGen
can offer a valuable alternative for synthesizing realistic waveforms and
provide a promising solution for the lack of labeled seismic data.
