@article {niksejel_obstransformer_2024,
	title = {OBSTransformer: A Deep-Learning Seismic Phase Picker for OBS Data Using Automated Labelling and Transfer Learning},
	journal = {Geophysical Journal International},
	year = {2024},
	pages = {ggae049},
	abstract = {Accurate seismic phase detection and onset picking are fundamental to seismological studies. Supervised deep-learning phase pickers have shown promise with excellent performance on land seismic data. Although it may be acceptable to apply them to OBS (Ocean Bottom Seismometer) data that are indispensable for studying ocean regions, they suffer from a significant performance drop. In this study, we develop a generalised transfer-learned OBS phase picker {\textendash} OBSTransformer, based on automated labelling and transfer learning. First, we compile a comprehensive dataset of catalogued earthquakes recorded by 423 OBSs from 11 temporary deployments worldwide. Through automated processes, we label the P and S phases of these earthquakes by analysing the consistency of at least three arrivals from four widely used machine learning pickers (EQTransformer, PhaseNet, Generalized Phase Detection, and PickNet), as well as the AIC (Akaike Information Criterion) picker. This results in an inclusive OBS dataset containing \~{}36,000 earthquake samples. Subsequently, we employ this dataset for transfer learning and utilize a well-trained land machine learning model {\textendash} EQTransformer as our base model. Moreover, we extract 25,000 OBS noise samples from the same OBS networks using the Kurtosis method, which are then used for model training alongside the labelled earthquake samples. Using three groups of test datasets at sub-global, regional, and local scales, we demonstrate that OBSTransformer outperforms EQTransformer. Particularly, the P and S recall rates at large distances (\&gt;200 km) are increased by 68\% and 76\%, respectively. Our extensive tests and comparisons demonstrate that OBSTransformer is less dependent on the detection/picking thresholds and is more robust to noise levels.},
	issn = {0956-540X},
	doi = {10.1093/gji/ggae049},
	url = {https://doi.org/10.1093/gji/ggae049},
	author = {Niksejel, Alireza and Zhang, Miao}
}
@article {cones_offshore_2024,
	title = {Offshore windfarm construction elevates metabolic rate and increases predation vulnerability of a key marine invertebrate},
	journal = {Environmental Pollution},
	volume = {360},
	year = {2024},
	month = {11/2024},
	pages = {124709},
	abstract = {A global increase in offshore windfarm development is critical to our renewable energy future. Yet, widespread construction plans have generated substantial concern for impacts to co-occurring organisms and the communities they form. Pile driving construction, prominent in offshore windfarm development, produces among the highest amplitude sounds in the ocean creating widespread concern for a diverse array of taxa. However, studies addressing ecologically key species are generally lacking and most research is disparate, failing to integrate across response types (e.g., behavior, physiology, and ecological interactions), particularly in situ. The lack of integrative field studies presents major challenges to understand or mitigate actual impacts of offshore wind development. Here, we examined critical behavioral, physiological, and antipredator impacts of actual pile driving construction on the giant sea scallop (Placopecten magellanicus). Benthic taxa including bivalves are of particular concern because they are sound-sensitive, cannot move appreciable distances away from the stressor, and support livelihoods as one of the world{\textquoteright}s most economically and socially important fisheries. Overall, pile driving sound impacted scallops across a series of behavioral and physiological assays. Sound-exposed scallops consistently reduced their valve opening (22\%), resulting in lowered mantle water oxygen levels available to the gills. Repeated and rapid valve adductions led to a 56\% increase in metabolic rates relative to pre-exposure baselines. Consequently, in response to predator stimuli, sound-exposed scallops displayed a suite of significantly weaker antipredator behaviors including fewer swimming events and shorter time-to-exhaustion. These results show aquatic construction activities can induce metabolic and ecologically relevant changes in a key benthic animal. As offshore windfarm construction accelerates globally, our field-based study highlights that spatial overlap with benthic taxa may cause substantial metabolic changes, alter important fisheries resources, and ultimately could lead to increased predation.},
	keywords = {Metabolism, noise, Predation, Scallop, Sound pollution},
	issn = {0269-7491},
	doi = {10.1016/j.envpol.2024.124709},
	url = {https://www.sciencedirect.com/science/article/pii/S0269749124014234},
	author = {Cones, Seth F. and J{\'e}z{\'e}quel, Youenn and Jarriel, Sierra and Aoki, Nad{\`e}ge and Brewer, Hannah and Collins, John and Chauvaud, Laurent and Mooney, T. Aran}
}
@article {chen_origin_2024,
	title = {On the Origin of the Hawaiian Swell: Lithosphere and Asthenosphere Seismic Structure From Rayleigh Wave Dispersion},
	journal = {Journal of Geophysical Research: Solid Earth},
	volume = {129},
	number = {7},
	year = {2024},
	note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2024JB029407},
	month = {07/2024},
	pages = {e2024JB029407},
	abstract = {In this study, we revisit the shear-wave velocity structure of the lithosphere and asthenosphere surrounding the Hawaiian hotspot and Hawaiian swell using Rayleigh wave data spanning periods of 20{\textendash}125 s from the PLUME project. A primary goal of this investigation is to probe the origin of the Hawaiian swell and the mechanism that elevates the topography, providing insights into mantle dynamics beneath hotspot swells. In the shear velocity model, the 30{\textendash}70 km depth range is largely featureless with weak and local anomalies, indicating that the elevation of the Hawaiian swell cannot be attributed to upper lithospheric reheating or replacement. In contrast, at 80{\textendash}150 km depth, a pronounced region of anomalously low velocities is well-resolved, with the lowest velocities found beneath the Hawaii-Maui-Molokai part of the island chain. Minimum shear velocities are approximately 4.0 km/s at 100{\textendash}120 km depth, which is an \~{}8\%-10\% velocity decrease relative to the surrounding velocities away from the swell. This pattern suggests that hot, buoyant mantle from deeper plume sources laterally spread out near the top of the normal oceanic asthenosphere. We find that the low-velocity pattern in the asthenosphere exhibits a strong correlation with the overall shape of the Hawaiian swell topography. Assuming that density anomalies are proportional to shear velocity anomalies, we demonstrate that the anomalous elevation of the swell can be explained by the uplift of a 30-km-thick elastic plate loaded from below by this buoyant, low-seismic-velocity layer in the asthenosphere.},
	keywords = {Hawaiian swell, lithosphere and asthenosphere, oceanic hotspot, Rayleigh wave, shear-wave velocity, two-plane wave method},
	issn = {2169-9356},
	doi = {10.1029/2024JB029407},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2024JB029407},
	author = {Chen, Kai-Xun and Forsyth, Donald W.}
}
@article {yang_optimal_2023,
	title = {Optimal stacking of noise cross-correlation functions},
	journal = {Geophysical Journal International},
	volume = {232},
	number = {3},
	year = {2023},
	month = {mar},
	pages = {1600{\textendash}1618},
	abstract = {Cross-correlations of ambient seismic noise are widely used for seismic velocity imaging, monitoring and ground motion analyses. A typical step in analysing noise cross-correlation functions (NCFs) is stacking short-term NCFs over longer time periods to increase the signal quality. Spurious NCFs could contaminate the stack, degrade its quality and limit its use. Many methods have been developed to improve the stacking of coherent waveforms, including earthquake waveforms, receiver functions and NCFs. This study systematically evaluates and compares the performance of eight stacking methods, including arithmetic mean or linear stacking, robust stacking, selective stacking, cluster stacking, phase-weighted stacking, time{\textendash}frequency phase-weighted stacking, Nth-root stacking and averaging after applying an adaptive covariance filter. Our results demonstrate that, in most cases, all methods can retrieve clear ballistic or first arrivals. However, they yield significant differences in preserving the phase and amplitude information. This study provides a practical guide for choosing the optimal stacking method for specific research applications in ambient noise seismology. We evaluate the performance using multiple onshore and offshore seismic arrays in the Pacific Northwest region. We compare these stacking methods for NCFs calculated from raw ambient noise (referred to as Raw NCFs) and from ambient noise normalized using a one-bit clipping time normalization method (referred to as One-bit NCFs). We evaluate six metrics, including signal-to-noise ratios, phase dispersion images, convergence rate, temporal changes in the ballistic and coda waves, relative amplitude decays with distance and computational time. We show that robust stacking is the best choice for all applications (velocity tomography, monitoring and attenuation studies) using Raw NCFs. For applications using One-bit NCFs, all methods but phase-weighted and Nth-root stacking are good choices for seismic velocity tomography. Linear, robust and selective stacking methods are all equally appropriate choices when using One-bit NCFs for monitoring applications. For applications relying on accurate relative amplitudes, the linear, robust, selective and cluster stacking methods all perform well with One-bit NCFs. The evaluations in this study can be generalized to a broad range of time-series analysis that utilizes data coherence to perform ensemble stacking. Another contribution of this study is the accompanying open-source software package, StackMaster, which can be used for general purposes of time-series stacking.},
	issn = {0956-540X},
	doi = {10.1093/gji/ggac410},
	url = {https://doi.org/10.1093/gji/ggac410},
	author = {Yang, Xiaotao and Bryan, Jared and Okubo, Kurama and Jiang, Chengxin and Clements, Timothy and Denolle, Marine A}
}
@article {webb_optimizing_2023,
	title = {Optimizing Ocean Bottom Seismometers for Shallow Water Using Shielding and Horizontal Pressure Gradient Data},
	journal = {Journal of Geophysical Research: Solid Earth},
	volume = {128},
	number = {8},
	year = {2023},
	note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2023JB026356},
	pages = {e2023JB026356},
	abstract = {We show large {\textquotedblleft}shields{\textquotedblright} can significantly reduce horizontal component noise levels for ocean bottom seismometers by shielding the seismic sensors from shaking due to ocean currents. However, at short period (<1 s) some noise from vibration or rocking of the shield under current can partly couple through the ground causing peaks in the noise spectra. Deformation of the seafloor under ocean wave loading ({\textquotedblleft}compliance{\textquotedblright}) raises noise levels at long period (>5 s) for both vertical and horizontal components at shallow water sites. Pressure gauge data is now routinely used to predict and remove this source of noise on vertical component data. We have developed horizontal pressure gradient (HPG) gauges to measure and remove this source of noise from horizontal component data. The combination of HPG data and shielding enables low noise long period seismic observations at otherwise very noisy shallow seafloor sites.},
	keywords = {instrumentation, seafloor, seismology},
	issn = {2169-9356},
	doi = {10.1029/2023JB026356},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2023JB026356},
	author = {Webb, Spahr C. and Barclay, Andrew H.}
}
@article {laske_observations_2021,
	title = {Observations of Earth{\textquoteright}s Normal Modes on Broadband Ocean Bottom Seismometers},
	journal = {Frontiers in Earth Science},
	volume = {9},
	year = {2021},
	month = {06/2021},
	abstract = {It is generally thought that high noise levels in the oceans inhibit the observation of long-period earthquake signals such as Earth{\textquoteright}s normal modes on ocean bottom seismometers (OBSs). Here, we document the observation of Earth{\textquoteright}s gravest modes at periods longer than 500~s (or frequencies below 2~mHz). We start with our own 2005{\textendash}2007 Plume-Lithosphere-Undersea-Mantle Experiment (PLUME) near Hawaii that deployed a large number of broadband OBSs for the first time. We collected high-quality normal mode spectra for the great November 15, 2006 Kuril Islands earthquake on multiple OBSs. The random deployment of instruments from different OBS groups allows a direct comparison between different broadband seismometers. For this event, mode S06 (1.038~mHz) consistently rises above the background noise at all OBSs that had a Nanometrics Trillium T-240 broadband seismometer. We also report observations of other deployments in the Pacific ocean that involved instruments of the U.S. OBS Instrument Pool (OBSIP) where we observe even mode S04 (0.647~mHz). Earth{\textquoteright}s normal modes were never the initial target of any OBS deployment, nor was any other ultra-low-frequency signal. However, given the high costs of an OBS campaign, the fact that data are openly available to future investigators not involved in the campaign, and the fact that seismology is evolving to investigate ever-new signals, this paper makes the case that the investment in a high-quality seismic sensor may be a wise one, even for a free-fall OBS.},
	issn = {2296-6463},
	doi = {10.3389/feart.2021.679958},
	url = {https://www.frontiersin.org/articles/10.3389/feart.2021.679958},
	author = {Laske, Gabi}
}
@article {wei_obs_2020,
	title = {An OBS Array to Investigate Offshore Seismicity during the 2018~Kilauea Eruption},
	journal = {Seismological Research Letters},
	volume = {92},
	number = {1},
	year = {2020},
	month = {01/2021},
	pages = {603{\textendash}612},
	abstract = {On 3 May 2018, Kilauea Volcano, one of the most active volcanoes in the world, entered a new eruptive phase because of a dike intrusion in the East Rift zone. One day later, an Mw~6.9 earthquake, which was likely trigged by the dike intrusion, occurred in the submarine south flank of Kilauea Volcano. In mid-July, an ocean-bottom seismometer (OBS) array consisting of 12 stations was deployed on the submarine south flank of Kilauea Volcano to monitor the aftershocks and lava{\textendash}water interaction near the ocean entry. Eleven OBSs were recovered in mid-September. Preliminary evaluation of the data reveals a large number of seismic and acoustic events, which provide a valuable dataset for understanding flank deformation and stability as well as lava{\textendash}water interaction. Here, we introduce this dataset and document notable instrument malfunctions along with some initial seismic and acoustic observations.},
	issn = {0895-0695},
	doi = {10.1785/0220200206},
	url = {https://doi.org/10.1785/0220200206},
	author = {Wei, XiaoZhuo and Shen, Yang and Caplan-Auerbach, Jacqueline and Morgan, Julia K.}
}
@article {fredrickson_optimizing_2019,
	title = {Optimizing Sensor Configurations for the Detection of Slow-Slip Earthquakes in Seafloor Pressure Records, Using the Cascadia Subduction Zone as a Case Study},
	journal = {Journal of Geophysical Research: Solid Earth},
	volume = {124},
	number = {12},
	year = {2019},
	note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019JB018053},
	pages = {13504{\textendash}13531},
	abstract = {We present seafloor pressure records from the Cascadia Subduction Zone, alongside oceanographic and geophysical models, to evaluate the spatial uniformity of bottom pressure and optimize the geometry of sensor networks for resolving offshore slow-slip transients. Seafloor pressure records from 2011 to 2015 show that signal amplitudes are depth-dependent, with tidally filtered and detrended root-mean-squares of <2 cm on the abyssal plain and >6 cm on the continental shelf. This is consistent with bottom pressure predictions from circulation models and comparable to deformation amplitudes from offshore slow slip observed in other subduction zones. We show that the oceanographic component of seafloor pressure can be reduced to <=1-cm root-mean-square by differencing against a reference record from a similar depth, under restrictions that vary with depth. Instruments at 100{\textendash}250 m require depths matched within 10 m at separations of <100 km, while locations deeper than 1,400 m are broadly comparable over separations of at least 300 km. Despite the significant noise reduction from this method, no slow slip was identified in the dataset, possibly due to poor spatiotemporal instrument coverage, nonideal deployment geometry, and limited depth-matched instruments. We use forward predictions of deformation from elastic half-space models and hindcast pressure from circulation models to generate synthetic slow-slip observational records and show that a range of slip scenarios produce resolvable signals under depth-matched differencing. For future detection of offshore slow slip in Cascadia, we recommend a geometry in which instruments are deployed along isobaths to optimize corrections for oceanographic signals.},
	keywords = {Cascadia Subduction Zone, geodesy, ocean circulation, seafloor pressure, slow-slip earthquake},
	issn = {2169-9356},
	doi = {10.1029/2019JB018053},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2019JB018053},
	author = {Fredrickson, Erik K. and Wilcock, William S. D. and Schmidt, David A. and MacCready, Parker and Roland, Emily and Kurapov, Alexander L. and Zumberge, Mark A. and Sasagawa, Glenn S.}
}
@article {Flores2017,
	title = {Observations of Seismicity and Ground Motion in the Northeast U.S. Atlantic Margin from Ocean-Bottom Seismometer Data},
	journal = {Seismological Research Letters},
	volume = {88},
	number = {1},
	year = {2017},
	month = {jan},
	pages = {23{\textendash}31},
	issn = {0895-0695},
	doi = {10.1785/0220160079},
	url = {https://pubs.geoscienceworld.org/srl/article/88/1/23-31/314313},
	author = {Flores, Claudia H. and ten Brink, Uri S. and McGuire, Jeffrey J. and Collins, John A.}
}
@article {Doran2017,
	title = {Ocean-Bottom Seismometer Instrument Orientations via Automated Rayleigh-Wave Arrival-Angle Measurements},
	journal = {Bulletin of the Seismological Society of America},
	volume = {107},
	number = {2},
	year = {2017},
	month = {apr},
	pages = {691{\textendash}708},
	issn = {0037-1106},
	doi = {10.1785/0120160165},
	url = {https://pubs.geoscienceworld.org/bssa/article/107/2/691-708/354158},
	author = {Doran, Adrian K. and Laske, Gabi}
}
@article {Yeck2017,
	title = {Offshore Rayleigh group velocity observations of the South Island, New Zealand, from ambient noise data},
	journal = {Geophysical Journal International},
	volume = {209},
	number = {2},
	year = {2017},
	month = {may},
	pages = {827{\textendash}841},
	issn = {0956-540X},
	doi = {10.1093/gji/ggx054},
	url = {https://academic.oup.com/gji/article/209/2/827/2998716},
	author = {Yeck, William L. and Sheehan, Anne F. and Stachnik, Joshua C. and Lin, Fan-Chi}
}
@article {Peureux2016,
	title = {Ocean bottom pressure records from the Cascadia array and short surface gravity waves},
	journal = {Journal of Geophysical Research: Oceans},
	volume = {121},
	number = {5},
	year = {2016},
	month = {may},
	pages = {2862{\textendash}2873},
	issn = {2169-9275},
	doi = {10.1002/2015JC011580},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/2015JC011580},
	author = {Peureux, Charles and Ardhuin, Fabrice}
}
@article {Berger2016,
	title = {An ocean bottom seismic observatory with near real-time telemetry},
	journal = {Earth and Space Science},
	volume = {3},
	number = {2},
	year = {2016},
	month = {feb},
	pages = {68{\textendash}77},
	issn = {2333-5084},
	doi = {10.1002/2015EA000137},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/2015EA000137},
	author = {Berger, J. and Laske, G. and Babcock, J. and Orcutt, J.}
}
@article {Bowden2016,
	title = {Offshore Southern California lithospheric velocity structure from noise cross-correlation functions},
	journal = {Journal of Geophysical Research: Solid Earth},
	volume = {121},
	number = {5},
	year = {2016},
	month = {may},
	pages = {3415{\textendash}3427},
	issn = {21699313},
	doi = {10.1002/2016JB012919},
	url = {http://doi.wiley.com/10.1002/2016JB012919},
	author = {Bowden, D. C. and Kohler, M. D. and Tsai, V. C. and Weeraratne, D. S.}
}
@article {Sumy2015,
	title = {Ocean-Bottom Seismograph Performance during the Cascadia Initiative},
	journal = {Seismological Research Letters},
	volume = {86},
	number = {5},
	year = {2015},
	month = {sep},
	pages = {1238{\textendash}1246},
	issn = {0895-0695},
	doi = {10.1785/0220150110},
	url = {https://pubs.geoscienceworld.org/srl/article/86/5/1238-1246/315519},
	author = {Sumy, Danielle F. and Lodewyk, Jessica A. and Woodward, Robert L. and Evers, Brent}
}
@article {Becel2015,
	title = {Origin of dipping structures in fast-spreading oceanic lower crust offshore Alaska imaged by multichannel seismic data},
	journal = {Earth and Planetary Science Letters},
	volume = {424},
	year = {2015},
	month = {aug},
	pages = {26{\textendash}37},
	issn = {0012821X},
	doi = {10.1016/j.epsl.2015.05.016},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0012821X15003052},
	author = {B{\'e}cel, Anne and Shillington, Donna J. and Nedimovi{\'c}, Mladen R. and Webb, Spahr C. and Kuehn, Harold}
}
