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  • 1
    Publication Date: 2019
    Description: Abstract Geodetic earthquake early warning (EEW) algorithms complement point‐source seismic systems by estimating fault‐finiteness and unsaturated moment magnitude for the largest, most damaging earthquakes. Because such earthquakes are rare, it has been difficult to demonstrate that geodetic warnings improve ground motion estimation significantly. Here, we quantify and compare timeliness and accuracy of magnitude and ground motion estimates in simulated real time from seismic and geodetic observations for a suite of globally‐distributed, large earthquakes. Magnitude solutions saturate for the seismic EEW algorithm (we use ElarmS) while the ElarmS‐triggered Geodetic Alarm System (G‐larmS) reduces the error even for its first solutions. Shaking intensity (MMI) time series calculated for each station and each event are assessed based on MMI‐threshold crossings, allowing us to accurately characterize warning times per‐station. We classify alerts and find that MMI 4 thresholds result in true positive (TP) alerts for only 13.7% of sites exceeding MMI4 with a median warning time of 18.9 s for ElarmS, while G‐larmS issues TP alerts for 52.3% of all sites exceeding MMI4 with a significantly longer median warning time of 55.8 s. The geodetic EEW system reduces the number of missed alerts for a threshold of MMI 4 from 48.7% to 19.2% for all sites, but also increases the number of false positive alerts from 1.2% to 13.4% of all sites. By quantifying increased accuracy in magnitude, ground motion estimation, and alert timeliness; we demonstrate that finite‐fault geodetic algorithms add significant value, including better cost savings performance, to point‐source seismic EEW systems.
    Print ISSN: 2169-9313
    Electronic ISSN: 2169-9356
    Topics: Geosciences , Physics
    Published by Wiley on behalf of American Geophysical Union (AGU).
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  • 2
    Publication Date: 2017-08-17
    Description: GNSS-based earthquake early warning (EEW) algorithms estimate fault-finiteness and unsaturated moment magnitude for the largest, most damaging earthquakes. Because large events are infrequent, algorithms are not regularly exercised and insufficiently tested on few available datasets. We use 1300 realistic, time-dependent, synthetic earthquakes on the Cascadia megathrust to rigorously test the Geodetic Alarm System. Solutions are reliable once six GNSS stations report static offsets, which we require for a “first alert.” Median magnitude and length errors are -0.15±0.24 units and -31±40% for the first alert, and -0.04±0.11 units and +7±31% for the final solution. We perform a coupled test of a seismic-geodetic EEW system using synthetic waveforms for a M w 8.7 scenario. Seismic point-source solutions result in severely underestimated PGA. Geodetic finite-fault solutions provide more accurate predictions at larger distances, thus increasing warning times. Hence, GNSS observations are essential in EEW to accurately characterize large (out-of-network) events and correctly predict ground motion.
    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
  • 4
    Publication Date: 2019-04-01
    Description: Geodetic earthquake early warning (EEW) algorithms complement point-source seismic systems by estimating fault-finiteness and unsaturated moment magnitude for the largest, most damaging earthquakes. Because such earthquakes are rare, it has been difficult to demonstrate that geodetic warnings improve ground motion estimation significantly. Here, we quantify and compare timeliness and accuracy of magnitude and ground motion estimates in simulated real time from seismic and geodetic observations for a suite of globally distributed, large earthquakes. Magnitude solutions saturate for the seismic EEW algorithm (we use ElarmS) while the ElarmS-triggered Geodetic Alarm System (G-larmS) reduces the error even for its first solutions. Shaking intensity (Modified Mercalli Intensity, MMI) time series calculated for each station and each event are assessed based on MMI threshold crossings, allowing us to accurately characterize warning times per station. We classify alerts and find that MMI 4 thresholds result in true positive alerts for only 13.7% of sites exceeding MMI 4 with a median warning time of 18.9 s for ElarmS, while G-larmS issues true positive alerts for 52.3% of all sites exceeding MMI 4 with a significantly longer median warning time of 55.8 s. The geodetic EEW system reduces the number of missed alerts for a threshold of MMI 4 from 48.7% to 19.2% for all sites, but also increases the number of false positive alerts from 1.2% to 13.4% of all sites. By quantifying increased accuracy in magnitude, ground motion estimation, and alert timeliness, we demonstrate that finite-fault geodetic algorithms add significant value, including better cost savings performance, to point-source seismic EEW systems for large earthquakes.
    Print ISSN: 2169-9313
    Electronic ISSN: 2169-9356
    Topics: Geosciences , Physics
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  • 5
    Publication Date: 2017-08-26
    Description: Global Navigation Satellite Systems (GNSS)-based earthquake early warning (EEW) algorithms estimate fault finiteness and unsaturated moment magnitude for the largest, most damaging earthquakes. Because large events are infrequent, algorithms are not regularly exercised and insufficiently tested on few available data sets. We use 1300 realistic, time-dependent, synthetic earthquakes on the Cascadia megathrust to rigorously test the Geodetic Alarm System. Solutions are reliable once six GNSS stations report static offsets, which we require for a “first alert.” Median magnitude and length errors are −0.15 ± 0.24 units and −31 ± 40% for the first alert, and −0.04 ± 0.11 units and +7 ± 31% for the final solution. We perform a coupled test of a seismic-geodetic EEW system using synthetic waveforms for a Mw8.7 scenario. Seismic point-source solutions result in severely underestimated peak ground acceleration. Geodetic finite-fault solutions provide more accurate predictions at larger distances, thus increasing warning times. Hence, GNSS observations are essential in EEW to accurately characterize large (out-of-network) events and correctly predict ground motion. ©2017. American Geophysical Union. All Rights Reserved.
    Print ISSN: 0094-8276
    Electronic ISSN: 1944-8007
    Topics: Geosciences , Physics
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  • 6
    Publication Date: 2018-11-20
    Description: Volcanic plumes are challenging to detect and characterize rapidly, but insights into processes such as hail formation or ash aggregation are valuable to hazard forecasts during volcanic crises. Global Navigation Satellite System (GNSS, which includes GPS) signals traveling from satellites to ground receivers can be disturbed by volcanic plumes. To date, two effects aiding plume detection from GNSS observations have been described: (a) ash-rich plumes scatter the signal, lowering the signal-to-noise ratio (SNR), and (b) some plumes refract and thus delay GNSS signals. Using GNSS data from the VEI 4 2011 Grímsvötn eruption, we show that tephra and water contents of plumes distinctly affect SNR and phase residuals. The signals suggest high-altitude freezing of plume water into volcanic hail—corroborated by 1-D modeling and volcanic hail deposits. Combining GNSS SNR and phase residual analyses is valuable for detecting processes that rapidly scrub fine ash out of the atmosphere. ©2018. American Geophysical Union. All Rights Reserved.
    Print ISSN: 0094-8276
    Electronic ISSN: 1944-8007
    Topics: Geosciences , Physics
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  • 7
    Publication Date: 2014-10-01
    Print ISSN: 2169-9313
    Electronic ISSN: 2169-9356
    Topics: Geosciences , Physics
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  • 8
    Publication Date: 2014-01-01
    Print ISSN: 0098-3004
    Electronic ISSN: 1873-7803
    Topics: Geosciences , Computer Science
    Published by Elsevier
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  • 9
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    In:  XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
    Publication Date: 2023-07-07
    Description: Erebus volcano is Antarctica’s most active volcano. Inside the 200 m-deep Inner Crater is a convecting phonolitic lava lake that has been stable for 50 years. Eruptive activity is characterized by small Strombolian explosions from the lava lake and adjacent vents, which occasionally eject lava bombs onto the crater rim. Global Navigation Satellite System (GNSS) stations have been used to observe Erebus deformation for 24 years, first in campaign mode and since the mid-2000s with seven continuous stations. Seismic activity at Erebus has been observed with a short-period seismic network since 1980 and broadband seismometers since 2003. Seismic surveys, such as the 2007-2008 TOMO Erebus experiment, have been used to densify these observations. Here, we use seismic and geodetic observations to analyze Erebus deformation from 10s of minutes to decadal time scales. Tilt derived from the broadband seismic data shows no notable deformation prior to Strombolian explosions, indicating hydrostatic adjustments of the magma column to rising gas slugs in the lava lake-conduit system. The GNSS data show long-term subsidence of Ross Island, consistent with a response of the lithosphere to Erebus loading over the last 20 ka as suggested by modeling. We also discover multi-year inflation and deflation cycles of the summit area in the GNSS data that we link to dynamics of the shallow magmatic system. The most recent inflation event lasted from November 2020 until March 2022. Based on prior inflation periods, this might herald an increase in the number and size of Strombolian eruptions.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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  • 10
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    In:  XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
    Publication Date: 2023-08-09
    Description: Commonly, deformation analysis or inversion software is not easily expandable to include new geodetic models or geodetic data types. We developed an open source, extensible Python-based framework for forward and inverse modeling of crustal deformation sources that abstracts from specific source model implementations, data types and inversion methods. Within this framework, we implement the most common volcanic source models which can be combined to model and analyze multi-source deformation. VMOD supports the most common geodetic datasets (GNSS, InSAR, and tilt) and others can be added with little effort. Non-linear least squares and Markov Chain Monte-Carlo (MCMC) Bayesian inversions are supported, as well as joint inversions of different types of data. VMOD’s structure allows for easy integration with Python implementations of new geodetic models. We benchmark the forward models against other published results and the inversion approaches against other implementations. We apply VMOD to analyze deformation at Unimak Island, Alaska, observed with continuous and campaign GPS, and InSAR images from the Sentinel-1 satellite mission. These data show an inflation pattern in Westdahl volcano and indicate subsidence in Fisher caldera. Previously, these deformation signals have been analyzed separately. We use VMOD to jointly invert the GPS and InSAR datasets to constrain the parameters of a single model that is composed of two sources. Our results revealed a contracting sill under Fisher caldera and an inflating spherical source underneath Westdahl volcano. This framework is part of the NSF PREEVENTS Eruption Forecasting project and will be published as an open source package.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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