Description: Current velocities of the upper water column along the cruise track of R/V Maria S. Merian cruise MSM122 were collected by a vessel-mounted 38 kHz RDI Ocean Surveyor ADCP. The ADCP transducer was located at 6.0 m below the water line. The instrument was operated in narrowband mode (WM10) with a bin size of 32.00 m, a blanking distance of 16.00 m, and a total of 50 bins, covering the depth range between 54.0 m and 1622.0 m. Heading, pitch and roll data from the ship's motion reference unit and the navigation data from the Global Positioning systems were used by the data acquisition software VmDAS internally to convert ADCP velocities into earth coordinates. Single-ping data were screened for bottom signals and, where appropriate, a bottom mask was manually processed. The ship's velocity was calculated from position fixes obtained by the Global Positioning System (GPS). Accuracy of the ADCP velocities mainly depends on the quality of the position fixes and the ship's heading data. Further errors stem from a misalignment of the transducer with the ship's centerline. Data post-processing included water track calibration of the misalignment angle (0.4211° +/- 0.4756°) and scale factor (0.9993 +/- 0.0088) of the Ocean Surveyor signal. The velocity data were averaged in time using an average interval of 60 s. Velocity quality flagging is based on different threshold criteria: Depth cells with ensemble-averaged percent-good values below 25% are marked as 'bad data'. Depth cells with velocities above 2.0 m/s are flagged as 'bad data'. Depth cells with a root-mean-square deviation between the measured ensemble-average velocity and a cell-wise running-mean velocity above 0.3 m/s are flagged as 'probably bad data'.

Description: Current velocities of the upper water column along the cruise track of R/V Maria S. Merian cruise MSM122 were collected by a vessel-mounted 75 kHz RDI Ocean Surveyor ADCP. The ADCP transducer was located at 6.0 m below the water line. The instrument was operated in narrowband mode (WM10) with a bin size of 8.00 m, a blanking distance of 8.00 m, and a total of 100 bins, covering the depth range between 22.0 m and 814.0 m. Heading, pitch and roll data from the ship's motion reference unit and the navigation data from the Global Positioning systems were used by the data acquisition software VmDAS internally to convert ADCP velocities into earth coordinates. The ship's velocity was calculated from position fixes obtained by the Global Positioning System (GPS). Accuracy of the ADCP velocities mainly depends on the quality of the position fixes and the ship's heading data. Further errors stem from a misalignment of the transducer with the ship's centerline. Data post-processing included water track calibration of the misalignment angle (-47.4000° +/- 0.4581°) and scale factor (1.0021 +/- 0.0089) of the Ocean Surveyor signal. The velocity data were averaged in time using an average interval of 60 s. Velocity quality flagging is based on different threshold criteria: Depth cells with ensemble-averaged percent-good values below 25% are marked as 'bad data'. Depth cells with velocities above 2.0 m/s are flagged as 'bad data'. Depth cells with a root-mean-square deviation between the measured ensemble-average velocity and a cell-wise running-mean velocity above 0.3 m/s are flagged as 'probably bad data'.

Description: Raw data acquired by position sensors on board RV MERIAN during expedition MSM122 were processed to receive a validated master track which can be used as reference of further expedition data. During MSM122 the motion reference unit Kongsberg SeaTex AS MRU-5 combined with Kongsberg SeaTex AS Seapath 320 and the GPS receivers Trimble SPS855 and SAAB R4 were used as navigation sensors. Data were downloaded from DAVIS SHIP data base (https://dship.bsh.de) with a resolution of 1 sec. Processing and evaluation of the data is outlined in the data processing report. Processed data are provided as a master track with 1 sec resolution derived from the position sensors' data selected by priority and a generalized track with a reduced set of the most significant positions of the master track.

Description: Raw data acquired by position sensors on board RV MERIAN during expedition MSM122 were processed to receive a validated master track which can be used as reference of further expedition data. During MSM122 the motion reference unit Kongsberg SeaTex AS MRU-5 combined with Kongsberg SeaTex AS Seapath 320 and the GPS receivers Trimble SPS855 and SAAB R4 were used as navigation sensors. Data were downloaded from DAVIS SHIP data base (https://dship.bsh.de) with a resolution of 1 sec. Processing and evaluation of the data is outlined in the data processing report. Processed data are provided as a master track with 1 sec resolution derived from the position sensors' data selected by priority and a generalized track with a reduced set of the most significant positions of the master track.

Description: 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