PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning

Abstract

Detecting phase arrivals and pinpointing the arrival times of seismic phases in seismograms is crucial for many seismological analysis workflows. For land station data machine learning methods have already found widespread adoption. However, deep learning approaches are not yet commonly applied to ocean bottom data due to a lack of appropriate training data and models. Here, we compiled a large and labeled ocean bottom seismometer dataset from 15 deployments in different tectonic settings, comprising ∼90,000 P and ∼63,000 S manual picks from 13,190 events and 355 stations. We adapted two popular deep learning networks, EQTransformer and PhaseNet, to include hydrophone recordings, either in isolation or in combination with the three seismometer components, and trained them with the waveforms in the new database. The performance is enhanced by employing transfer learning, where initial weights are derived from models trained with land earthquake data. Our final model, PickBlue, significantly outperforms neural networks trained with land stations and models trained without hydrophone data. The model achieves a mean absolute deviation (MAD) of 0.06 s for P waves and 0.10 s for S waves. We integrate our dataset and trained models into SeisBench to enable an easy and direct application in future deployments.