Deep Generative Model Conditioned by Phase Picks for Synthesizing Labeled Seismic Waveforms With Limited Data

Shortage of labeled seismic field data poses a significant challenge for deep-learning (DL)-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 may fail to capture features key for understanding the subsurface as in real seismic waveforms. In this study, we develop a DL-based generative model, PhaseGen, for synthesizing realistic seismic waveforms dictated by provided P- and S-wave arrival labels. Contrary to previous generative models that 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 labeled seismic dataset used for training a deep neural network for the phase picking task, and it is found that the model training using augmented datasets improves the picking performance. It is expected that PhaseGen can offer a valuable alternative for rapid seismic waveform synthesis and provide a promising solution for the lack of labeled seismic data.