PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning

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 dataset 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 ot 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 (MAD) of 0.05 s for P waves and 0.12 s for S waves. We integrate our dataset and trained models into SeisBench to enable an easy and direct application in future deployments

PickBlue: Seismic Phase Picking for Ocean Bottom Seismometers With Deep Learning

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

Pervasive Eclogitization Due to Brittle Deformation and Rehydration of Subducted Basement: Effects on Continental Recycling?

The buoyancy of continental crust opposes its subduction to mantle depths, except where mineral reactions substantially increase rock density. Sluggish kinetics limit such densification, especially in dry rocks, unless deformation and hydrous fluids intervene. Here we document how hydrous fluids in the subduction channel invaded lower crustal granulites at 50–60 km depth through a dense network of probably seismically induced fractures. We combine analyses of textures and mineral composition with thermodynamic modeling to reconstruct repeated stages of interaction, with pulses of high‐pressure (HP) fluid at 650–670°C, rehydrating the initially dry rocks to micaschists. SIMS oxygen isotopic data of quartz indicate fluids of crustal composition. HP growth rims in allanite and zircon show uniform U‐Th‐Pb ages of ∼65 Ma and indicate that hydration occurred during subduction, at eclogite facies conditions. Based on this case study in the Sesia Zone (Western Italian Alps), we conclude that continental crust, and in particular deep basement fragments, during subduction can behave as substantial fluid sinks, not sources. Density modeling indicates a bifurcation in continental recycling: Chiefly mafic crust, once it is eclogitized to >60%, are prone to end up in a subduction graveyard, such as is tomographically evident beneath the Alps at ∼550 km depth. By contrast, dominantly felsic HP fragments and mafic granulites remain positively buoyant and tend be incorporated into an orogen and be exhumed with it. Felsic and intermediate lithotypes remain positively buoyant even where deformation and fluid percolation allowed them to equilibrate at HP

Basal seismicity of the Whillans Ice Plain, West Antarctica: Insights into multi-scale basal heterogeneity, stick-slip sliding, and ice stream basal processes

Conditions at the base of an ice stream control the ability of basal material to resist ice sliding, which affects ice stream mass balance. Yet, basal properties are notoriously hard to constrain. Tiny magnitude ~-2 to -1 stick-slip basal icequakes occurring near or on the basal sliding surface hold valuable information about this basal environment. In this dissertation, I investigate spatiotemporal patterns in these basal icequakes occurring beneath the Whillans Ice Plain (WIP), in West Antarctica, and interpret these patterns to gain insight into basal material heterogeneity and temporally evolving basal conditions.In Chapter 2, I determine where basal icequakes happen beneath the entire WIP. I find spatially variable seismicity rates, with basal seismicity most common in a ~40 km wide area surrounding a dynamically important region where ice-plain-wide unstable slip nucleates. This result implicates icequake-generating bed conditions in large-scale ice stream stick-slip. Additionally, I propose that basal icequakes occur where basal erosion exposes over-consolidated till to the ice base.In Chapter 3, I use back-projection to detect basal icequakes beneath a small seismic network near the nucleation region. Here, basal icequakes occur in streaks elongated along ice flow and in conjunction with low-amplitude (~2m) undulating basal topography. These patterns suggest the presence of mega-scale glacial lineations (MSGL), elongate bedforms common on paleo-ice stream beds. One icequake streak may occur in a shallow trough beside an MSGL, suggesting that these icequakes occur in erosion-impacted lows between MSGL where over-consolidated till or stiff sediment outcrops contact the ice base.In Chapter 4, I analyze an improved basal icequake catalog generated by cross correlating icequakes detected in Chapter 3. I estimate icequake moment magnitudes of Mw = -2.1 to -1.2 and fault rupture areas of 1-100 m^2 for several large basal icequakes. Families of nearly-identical repeating basal icequakes continue for ice sliding distances of typically <0.5 m, and most <0.2 m. If this distance also represents the approximate size of an icequake-generating fault, then basal icequake faults have dimensions of cm to m. I explore evidence for ice-bed interface healing between unstable slip events. Lastly, I discuss four possible mechanisms for basal seismicity

AACSE earthquake catalog: January-August, 2019

Dataset includes monthly CSS database tables (aecevent, arrival, assoc, event, netmag, origerr, origin) and quakeml files.The Alaska Amphibious Community Seismic Experiment (AACSE) comprised 75 ocean bottom seismometers and 30 land stations and covered about 650 km along the segment of the subduction zone that includes Kodiak Island, the Alaska Peninsula and the Shumagin Islands between May 2018 and September 2019. This unprecedented offshore dataset has the potential to support a greatly enhanced earthquake catalog by both increasing the number of detected earthquakes and improving the accuracy of their source parameters. We use all available regional and AACSE campaign seismic data to compile an enhanced earthquake catalog for the region between Kodiak and Shumagin Islands including Alaska Peninsula (51-59N, 148-163W). We apply the same processing and reporting standards to additional picks and events as the Alaska Earthquake Center currently use for compilation of the authoritative regional earthquake catalog. This release includes earthquake catalogs for the time period between January 01 and August 31, 2019. We include monthly CSS database tables (aecevent, arrival, assoc, event, netmag, origerr, origin) and quakeml files. This material is based upon work supported by the U.S. Geological Survey under Grant No. G20AP00026. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey

AACSE earthquake catalog: May-December, 2018

The Alaska Amphibious Community Seismic Experiment (AACSE) comprised 75 ocean bottom seismometers and 30 land stations and covered about 650 km along the segment of the subduction zone that includes Kodiak Island, the Alaska Peninsula and the Shumagin Islands between May, 2018 and September, 2019 (Barcheck et al., 2020). This unprecedented dataset has the potential to support a greatly enhanced earthquake catalog by both increasing the number of detected earthquakes and improving the accuracy of their source parameters. We use all available regional and AACSE campaign seismic data to compile an enhanced earthquake catalog for the region between Kodiak and Shumagin Islands including Alaska Peninsula (51-59N, 148-163W). We apply the same processing and reporting standards to additional picks and seismic events as the Alaska Earthquake Center currently use for compilation of the authoritative regional earthquake catalog. This release includes earthquake catalogs for the time period between May 12 and December 31, 2018 (3829 events total 1132 of which are newly detected). We include monthly CSS database tables and quakeml files. This material is based upon work supported by the U.S. Geological Survey under Grant No. G20AP00026. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey.This material is based upon work supported by the U.S. Geological Survey under Grant No. G20AP00026. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey

Rupture speed dependence on initial stress profiles: Insights from glacier and laboratory stick-slip

© 2014 The Authors. Slow slip events are now well-established in fault and glacier systems, though the processes controlling slow rupture remain poorly understood. The Whillans Ice Plain provides a window into these processes through bi-daily stick-slip seismic events that displace an ice mass over 100 km long with a variety of rupture speeds observed at a single location. We compare the glacier events with laboratory experiments that have analogous loading conditions. Both systems exhibit average rupture velocities that increase systematically with the pre-rupture stresses, with local rupture velocities exhibiting large variability that correlates well with local interfacial stresses. The slip events in both cases are not time-predictable, but clearly slip-predictable. Local pre-stress may control rupture behavior in a range of frictional failure events, including earthquakes

PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning

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 dataset 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 ot 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 (MAD) of 0.05 s for P waves and 0.12 s for S waves. We integrate our dataset 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 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

Rupture speed dependence on initial stress profiles: Insights from glacier and laboratory stick-slip

AbstractSlow slip events are now well-established in fault and glacier systems, though the processes controlling slow rupture remain poorly understood. The Whillans Ice Plain provides a window into these processes through bi-daily stick-slip seismic events that displace an ice mass over 100 km long with a variety of rupture speeds observed at a single location. We compare the glacier events with laboratory experiments that have analogous loading conditions. Both systems exhibit average rupture velocities that increase systematically with the pre-rupture stresses, with local rupture velocities exhibiting large variability that correlates well with local interfacial stresses. The slip events in both cases are not time-predictable, but clearly slip-predictable. Local pre-stress may control rupture behavior in a range of frictional failure events, including earthquakes