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  • 1
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    In:  Eos Trans. AGU, Luxembourg, National Academy of Sciences of the USA, vol. 86, no. 32, pp. 293 & 297, pp. B05311, (ISSN: 1340-4202)
    Publication Date: 2005
    Description: Subduction zones generate the world's largest and most destructive earthquakes and most of the world' s destructive tsunamis, as has been recently shown by the devastating Andaman-Sumatra event on 26 December 2004. Understanding the factors leading to Earth's largest and most destructive earthquakes is not only an "obviously important" goal, as stated in the U.S. National Science Foundation's Margins Science Report 2004, but it is also an "utmost important" goal for the whole geoscience community. Interrelated with this topic are still unsolved questions in seismology: Why do subduction zones occasionally generate the largest known (Mw 〉 9) earthquakes? And why are only a few subduction zones capable of generating Mw ~= 9 earthquakes while the rest only produce up to Mw ~= 7.5?
    Keywords: Deep seismic sounding (espec. cont. crust) ; TIPTEQ ; GFZ ; Chile ; Subduction zone ; Project report/description ; Earthquake asperities ; Geothermics ; OBS ; Reflection seismics ; Friction ; Seismic arrays ; Fracture ; NAZCA ; Physical properties of rocks ; Valdivia ; Seismology ; Gravimetry, Gravitation ; Geodesy ; Modelling ; 8170 ; Tectonophysics: ; Subduction ; zone ; processes ; 7230 ; Seismology: ; Seismicity ; and ; tectonics ; 7240 ; Seismology: ; Subduction ; zones
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  • 2
    Publication Date: 2016-06-08
    Description: The rapid discharge of gas and rock fragments during volcanic eruptions generates acoustic infrasound. Here we present results from the inversion of infrasound signals associated with small and moderate gas-and-ash explosions at Santiaguito volcano, Guatemala, to retrieve the time history of mass eruption rate at the vent. Acoustic waveform inversion is complemented by analyses of thermal infrared imagery to constrain the volume and rise dynamics of the eruption plume. Finally, we combine results from the two methods in order to assess the bulk density of the erupted mixture, constrain the timing of the transition from a momentum-driven jet to a buoyant plume, and to evaluate the relative volume fractions of ash and gas during the initial thrust phase. Our results demonstrate that eruptive plumes associated with small-to-moderate size explosions at Santiaguito only carry minor fractions of ash, suggesting that these events may not involve extensive magma fragmentation in the conduit.
    Print ISSN: 0094-8276
    Electronic ISSN: 1944-8007
    Topics: Geosciences , Physics
    Published by Wiley on behalf of American Geophysical Union (AGU).
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  • 3
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    American Association for the Advancement of Science (AAAS)
    Publication Date: 2007-06-09
    Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Rietbrock, Andreas -- New York, N.Y. -- Science. 2007 Jun 8;316(5830):1439-40.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Earth and Ocean Sciences, Liverpool University, Liverpool L69 3GP, UK. A.Rietbrock@liverpool.ac.uk〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/17556575" target="_blank"〉PubMed〈/a〉
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 4
    Publication Date: 2017-07-23
    Description: ABSTRACT We investigate fracture induced attenuation anisotropy in a cluster of events from a microseismic dataset acquired during hydraulic fracture stimulation. The dataset contains 888 events of magnitude −3.0 to 0.0. We use a log-spectral-amplitude-ratio method to estimate change in t * over a half hour time period where fluid is being injected and an increase in fracturing from S-wave splitting analysis has been previously inferred. A Pearson's correlation analysis is used to assess whether or not changes in attenuation with time are statistically significant. P-waves show no systematic change in t * during this time. In contrast, S-waves polarised perpendicular to the fractures show a clear and statistically significant increase with time, whilst S-waves polarised parallel to the fractures show a weak negative trend. We also compare t * between the two S-waves, finding an increase in Δ t * with time. A poroelastic rock physics model of fracture-induced attenuation anisotropy is used to interpret the results. This model suggests that the observed changes in t* are related to an increase in fracture density of up to 0:04. This is much higher than previous estimates of 0:025 ± 0:002 based on S-wave velocity anisotropy, but there is considerably more scatter in the attenuation measurements. This could be due to the added sensitivity of attenuation measurement to non-aligned fractures, fracture shape, and fluid properties. Nevertheless, this pilot study shows that attenuation measurements are sensitive to fracture properties such as fracture density and aspect ratio. This article is protected by copyright. All rights reserved
    Print ISSN: 0016-8025
    Electronic ISSN: 1365-2478
    Topics: Geosciences , Physics
    Published by Wiley
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  • 5
    Publication Date: 2017-08-05
    Description: Phyllosilicate-rich rocks which commonly occur within fault zones cause seismic velocity anisotropy. However, anisotropy is not always taken into account in seismic imaging and the extent of the anisotropy is often unknown. Laboratory measurements of the velocity anisotropy of fault zone rocks and gouge from the Carboneras fault zone in SE Spain indicate 10-15% velocity anisotropy in the gouge and 35-50% anisotropy in the mica-schist protolith. Greater differences in velocity are observed between the fast and slow directions in the mica-schist rock than between the gouge and the slow direction of the rock. This implies that the orientation of the anisotropy with respect to the fault is key in imaging the fault seismically. For example, for fault-parallel anisotropy, a significantly greater velocity contrast between fault gouge and rock will occur along the fault than across it, highlighting the importance
    Print ISSN: 0094-8276
    Electronic ISSN: 1944-8007
    Topics: Geosciences , Physics
    Published by Wiley on behalf of American Geophysical Union (AGU).
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  • 6
    Publication Date: 2018-02-10
    Description: Effective monitoring is an essential part of identifying and mitigating volcanic hazards. In the submarine environment this is more difficult than onshore because observations are typically limited to land-based seismic networks and infrequent shipboard surveys. Since the first recorded eruption in 1939, the Kick-‘em-Jenny (KeJ) volcano, located 8km off northern Grenada, has been the source of 13 episodes of T-phase signals. These distinctive seismic signals, often coincident with heightened body-wave seismicity, are interpreted as extrusive eruptions. They have occurred with a recurrence interval of around a decade, yet direct confirmation of volcanism has been rare. By conducting new bathymetric surveys in 2016 and 2017 and reprocessing 4 legacy datasets spanning 30 years we present a clearer picture of the development of KeJ through time. Processed grids with a cell size of 5m and vertical precision on the order of 1-4m allow us to correlate T-phase episodes with morphological changes at the volcano's edifice. In the time-period of observation 7.09x10 6 m 3 of material has been added through constructive volcanism – yet 5 times this amount has been lost through landslides. Limited recent magma production suggests that KeJ may be susceptible to larger eruptions with longer repeat times than have occurred during the study interval, behavior more similar to sub-aerial volcanism in the arc than previously thought. T-phase signals at KeJ have a varied origin and are unlikely to be solely the result of extrusive submarine eruptions. Our results confirm the value of repeat swath bathymetry surveys in assessing submarine volcanic hazards.
    Electronic ISSN: 1525-2027
    Topics: Chemistry and Pharmacology , Geosciences , Physics
    Published by Wiley on behalf of American Geophysical Union (AGU).
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  • 7
    Publication Date: 2019
    Description: 〈span〉〈div〉ABSTRACT〈/div〉Over the past two decades, the amount of available seismic data has increased significantly, fueling the need for automatic processing to use the vast amount of information contained in such data sets. Detecting seismicity in temporary aftershock networks is one important example that has become a huge challenge because of the high seismicity rate and dense station coverage. Additionally, the need for highly accurate earthquake locations to distinguish between different competing physical processes during the postseismic period demands even more accurate arrival‐time estimates of seismic phase. Here, we present a convolutional neural network (CNN) for classifying seismic phase onsets for local seismic networks. The CNN is trained on a small dataset for deep‐learning purposes (411 events) detected throughout northern Chile, typical for a temporary aftershock network. In the absence of extensive training data, we demonstrate that a CNN‐based automatic phase picker can still improve performance in classifying seismic phases, which matches or exceeds that of historic methods. The trained network is tested against an optimized short‐term average/long‐term average (STA/LTA) based method (〈a href="https://pubs.geoscienceworld.org/srl#rf25"〉Rietbrock 〈span〉et al.〈/span〉, 2012〈/a〉) in classifying phase onsets for a separate dataset of 3878 events throughout the same region. Based on station travel‐time residuals, the CNN outperforms the STA/LTA approach and achieves location residual distribution close to the ones obtained by manual inspection.〈/span〉
    Print ISSN: 0895-0695
    Electronic ISSN: 1938-2057
    Topics: Geosciences
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  • 8
    Publication Date: 2019
    Description: 〈span〉〈div〉ABSTRACT〈/div〉Over the past two decades, the amount of available seismic data has increased significantly, fueling the need for automatic processing to use the vast amount of information contained in such data sets. Detecting seismicity in temporary aftershock networks is one important example that has become a huge challenge because of the high seismicity rate and dense station coverage. Additionally, the need for highly accurate earthquake locations to distinguish between different competing physical processes during the postseismic period demands even more accurate arrival‐time estimates of seismic phase. Here, we present a convolutional neural network (CNN) for classifying seismic phase onsets for local seismic networks. The CNN is trained on a small dataset for deep‐learning purposes (411 events) detected throughout northern Chile, typical for a temporary aftershock network. In the absence of extensive training data, we demonstrate that a CNN‐based automatic phase picker can still improve performance in classifying seismic phases, which matches or exceeds that of historic methods. The trained network is tested against an optimized short‐term average/long‐term average (STA/LTA) based method (〈a href="https://pubs.geoscienceworld.org/srl#rf25"〉Rietbrock 〈span〉et al.〈/span〉, 2012〈/a〉) in classifying phase onsets for a separate dataset of 3878 events throughout the same region. Based on station travel‐time residuals, the CNN outperforms the STA/LTA approach and achieves location residual distribution close to the ones obtained by manual inspection.〈/span〉
    Print ISSN: 0895-0695
    Electronic ISSN: 1938-2057
    Topics: Geosciences
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  • 9
    Publication Date: 2019
    Description: 〈span〉〈div〉Abstract〈/div〉The Lesser Antilles arc is only one of two subduction zones where slow‐spreading Atlantic lithosphere is consumed. Slow‐spreading may result in the Atlantic lithosphere being more pervasively and heterogeneously hydrated than fast‐spreading Pacific lithosphere, thus affecting the flux of fluids into the deep mantle. Understanding the distribution of seismicity can help unravel the effect of fluids on geodynamic and seismogenic processes. However, a detailed view of local seismicity across the whole Lesser Antilles subduction zone is lacking. Using a temporary ocean‐bottom seismic network we invert for hypocenters and 1D velocity model. A systematic search yields a 27 km thick crust, reflecting average arc and back‐arc structures. We find abundant intraslab seismicity beneath Martinique and Dominica, which may relate to the subducted Marathon and/or Mercurius Fracture Zones. Pervasive seismicity in the cold mantle wedge corner and thrust seismicity deep on the subducting plate interface suggest an unusually wide megathrust seismogenic zone reaching ∼65  km depth. Our results provide an excellent framework for future understanding of regional seismic hazard in eastern Caribbean and the volatile cycling beneath the Lesser Antilles arc.〈/span〉
    Print ISSN: 0895-0695
    Electronic ISSN: 1938-2057
    Topics: Geosciences
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  • 10
    Publication Date: 2018-04-16
    Description: Dispersed P wave arrivals from intermediate-depth earthquakes in the Alaskan subduction zone provide insight into the low-velocity structure of the subducting oceanic crust. P wave arrivals from 41 earthquakes in the eastern section of the arc show significant guided wave dispersion, with high-frequency (〉1 Hz) energy delayed by up to 2–3 s. We simulate this dispersion using a 2-D finite difference waveform propagation model, systematically varying both P wave velocity and low-velocity layer thickness parameters to find the lowest misfit between the observed and synthetic waveforms. We infer a 6 to 10 km thick low-velocity layer with a P wave velocity contrast of 7–15% with the overriding mantle, velocities which cannot be entirely accounted for by metamorphosed mid-ocean ridge basalt compositions. We postulate that this structure is the remnant of the subducted Yakutat terrane, significantly thinned at depth by metamorphism or delamination of material during subduction. ©2018. The Authors.
    Print ISSN: 0094-8276
    Electronic ISSN: 1944-8007
    Topics: Geosciences , Physics
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