  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
an extensive 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 propose PickBlue, an adaptation ot the
two popular deep learning networks EQTransformer and PhaseNet. PickBlue joint
processes three seismometer recordings in conjunction with a hydrophone
component and is trained 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. PickBlue
significantly outperforms neural networks trained with land stations and models
trained without hydrophone data. The model achieves a mean absolute deviation
(MAD) of 0.05 s for P waves and 0.12 s for S waves. We integrate our dataset
and trained models into SeisBench to enable an easy and direct application in
future deployments.
