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

Deep generative model conditioned by phase picks for synthesizing labeled seismic waveforms with limited data