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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 (OBS) data set 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 of 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 of 0.05 s for P-waves and 0.12 s for S-waves, and we apply the picker on the Hikurangi Ocean Bottom Tremor and Slow Slip OBS deployment offshore New Zealand. We integrate our data set and trained models into SeisBench to enable an easy and direct application in future deployments. Key Points We assembled a database of ocean Bottom Seismometer (OBS) waveforms and manual P and S picks, on which we train PickBlue, a deep learning picker Our picker significantly outperforms pickers trained with land-based data with confidence values reflecting the likelihood of outlier picks The picker and database are available in the SeisBench platform, allowing easy and direct application to OBS traces and hydrophone records

Observations of spatio-temporal variability of the deep ocean are rare and little is known about occurrence of deep ocean mesoscale dynamics. Here, we make use of 2.5 years of time series data from three distributed sensor arrays, which acquired high-resolution temperature, pressure and sound speed data of the bottom layer offshore northern Chile. Estimating salinity and density from the direct observations enable access to the full spectrum of hydrographic variability from a multi-hourly to annual time scale and with average inter-station distances of less than 1 km. Analyses revealed interannual warming over the continental slope of 0.002 °C yr−1–0.003 °C yr−1, and could trace periodic hydrographic anomalies, likely related to coastal-trapped waves, as far as to the lower continental slope. A concurrent change in the shape of the warm anomalies and the rate of deep-sea warming that occurs with the crossing of the deep-sea trench suggests that the abyssal part of the eastern boundary current system off Chile does not extend past the deep sea trench. Furthermore, the comparison of anomaly timing and shape in between stations implies southwards flow over the mid to lower continental slope, centred closer to the trench.