@article {bornstein_pickblue_2024,
	title = {PickBlue: Seismic Phase Picking for Ocean Bottom Seismometers With Deep Learning},
	journal = {Earth and Space Science},
	volume = {11},
	number = {1},
	year = {2024},
	note = {_eprint: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2023EA003332},
	month = {11/2023},
	pages = {e2023EA003332},
	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 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.},
	keywords = {machine learning, OBS seismicity database, ocean bottom seismometer, onset determination, phase picking},
	issn = {2333-5084},
	doi = {10.1029/2023EA003332},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2023EA003332},
	author = {Bornstein, T. and Lange, D. and M{\"u}nchmeyer, J. and Woollam, J. and Rietbrock, A. and Barcheck, G. and Grevemeyer, I. and Tilmann, F.}
}
@article {eilon_sub-lithospheric_2022,
	title = {Sub-Lithospheric Small-Scale Convection Tomographically Imaged Beneath the Pacific Plate},
	journal = {Geophysical Research Letters},
	volume = {49},
	number = {18},
	year = {2022},
	note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022GL100351},
	month = {09/2022},
	pages = {e2022GL100351},
	abstract = {Small-scale convection beneath the oceanic plates has been invoked to explain off-axis nonplume volcanism, departure from simple seafloor depth-age relationships, and intraplate gravity lineations. We deployed 30 broadband ocean bottom seismometer stations on \~{}40 Ma Pacific seafloor in a region notable for gravity anomalies, measured by satellite altimetry, elongated parallel to plate motion. P-wave teleseismic tomography reveals alternating upper mantle velocity anomalies on the order of {\textpm}2\%, aligned with the gravity lineations. These features, which correspond to \~{}300{\textdegree}{\textendash}500{\textdegree}K lateral temperature contrast, and possible hydrous or carbonatitic partial melt, are{\textemdash}surprisingly{\textemdash}strongest between 150 and 260 km depth, indicating rapid vertical motions through a low-viscosity asthenospheric channel. Coherence and admittance analysis of gravity and topography using new multibeam bathymetry soundings substantiates the presence of mantle density variations, and forward modeling predicts gravity anomalies that qualitatively match observed lineations. This study provides observational support for small-scale convective rolls beneath the oceanic plates.},
	keywords = {asthenosphere, body wave tomography, mantle convection, ocean bottom seismometer, oceanic lithosphere},
	issn = {1944-8007},
	doi = {10.1029/2022GL100351},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2022GL100351},
	author = {Eilon, Zachary C. and Zhang, Lun and Gaherty, James B. and Forsyth, Donald W. and Russell, Joshua B.}
}
@article {harmon_tilt_2022,
	title = {Tilt Corrections for Normal Mode Observations on Ocean Bottom Seismic Data, an example from the PI-LAB experiment},
	journal = {Seismica},
	volume = {1},
	number = {1},
	year = {2022},
	note = {Number: 1},
	month = {11/2022},
	abstract = {Earth{\textquoteright}s normal modes are fundamental observations used in global seismic tomography to understand Earth structure. Land seismic station coverage is sufficient to constrain the broadest scale Earth structures. However, 70\% of Earth{\textquoteright}s surface is covered by the oceans, hampering our ability to observe variations in local mode frequencies that contribute to imaging small-scale structures. Broadband ocean bottom seismometers can record spheroidal modes to fill in gaps in global data coverage. Ocean bottom recordings are contaminated by signals from complex interactions between ocean and solid Earth dynamics at normal mode frequencies. We present a method for correcting tilt on broadband ocean bottom seismometers by rotation. The correction improves the ability of some instruments to observe spheroidal modes down to 0S4. We demonstrate this method using 15 broadband ocean bottom seismometers from the PI-LAB array. We measure normal mode peak frequency shifts and compare with 1-D reference mode frequencies and predictions from 3-D global models. Our measurements agree with the 3-D models for modes between 0S14 - 0S37 with small but significant differences. These differences likely reflect real Earth structure. This suggests incorporating ocean bottom normal mode measurements into global inversions will improve models of global seismic velocity structure.},
	keywords = {marine geophysics, normal modes, ocean bottom seismometer},
	issn = {2816-9387},
	doi = {10.26443/seismica.v1i1.196},
	url = {https://seismica.library.mcgill.ca/article/view/196},
	author = {Harmon, Nicholas and Laske, Gabi and Crawford, Wayne and Rychert, Catherine}
}
