@article {anderson_deformation_2025,
	title = {Deformation on Rainbow Massif, Mid-Atlantic Ridge, Illuminated With Microearthquakes Detected by Machine Learning},
	journal = {Geophysical Research Letters},
	volume = {52},
	number = {2},
	year = {2025},
	note = {_eprint: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2024GL111285},
	pages = {e2024GL111285},
	abstract = {Abstract Oceanic detachment fault systems are characteristic of slow-spreading mid-ocean ridges, where reduced magma supply leads to increased extension by faulting and exhumation of oceanic core complexes (OCCs). OCCs have complicated structure reflecting the interplay between magmatic, hydrothermal, and tectonic processes. We use microearthquake data from a 9-month ocean bottom seismometer deployment to image deformation structures in the Rainbow massif on the Mid-Atlantic Ridge. Using a machine-learning enabled workflow to obtain an earthquake catalog containing > {\vphantom} > \textbackslash68,000 events, we find seismicity occurred in distinct clusters that correlate with previously imaged velocity anomalies and dipping subsurface reflections. Our results are consistent with a dipping alteration front within the massif overlying late-stage intrusions and suggest a transpressional fault accommodates a non-transform offset north of the massif. Our results demonstrate OCCs continue to deform in a complex way after a detachment fault has been abandoned due to combined effects of tectonic stresses, magmatism, and alteration.},
	keywords = {detachment faults, machine learning, microearthquakes, oceanic core complex, slow spreading mid-ocean ridges},
	doi = {https://doi.org/10.1029/2024GL111285},
	author = {Anderson, Evan C. and Parnell-Turner, Ross and Sohn, Robert A. and Fan, Wenyuan}
}
@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.}
}
