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  Onsite Earthquake Early Warning: Predictive Models for Acceleration Response Spectra Considering Site Effects

Iaccarino, A. G., Picozzi, M., Bindi, D., Spallarossa, D. (2020): Onsite Earthquake Early Warning: Predictive Models for Acceleration Response Spectra Considering Site Effects. - Bulletin of the Seismological Society of America, 110, 3, 1289-1304.
https://doi.org/10.1785/0120190272

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 Creators:
Iaccarino, Antonio Giovanni1, Author
Picozzi, Matteo1, Author
Bindi, Dino2, Author              
Spallarossa, Daniele1, Author
Affiliations:
1External Organizations, ou_persistent22              
22.6 Seismic Hazard and Risk Dynamics, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146032              

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 Abstract: Onsite earthquake early warning (EEW) systems exploit predictive models relating features extracted over the P-wave window to S-wave ground-motion parameters. These models are usually calibrated considering recordings from multiple stations and combining datasets from different regions under the ergodic assumption. Here, we show that the local-site conditions can play a significant role in determining the performance of onsite EEW predictive models in terms of rates of false or missed alerts. Interestingly, if partially nonergodic models are implemented, as done in probabilistic seismic hazard analysis, the negative impact of local-site amplifications can be mitigated. We explore the influence of site effects for onsite EEW predictive models calibrated between the peak displacement (Pd) and integral squared velocity (Iv2) measured over a 3 s P-wave window, and the acceleration response spectra (RSA) at nine different periods T (T=0.1, 0.15, 0.2, 0.3, 0.5, 0.75, 1.0, 1.5, and 2.0 s). We consider 58 earthquakes with magnitudes between Mw 3.7 and 6.5, belonging to the 2016–2017 central Italy seismic sequence that have been recorded by 100 accelerometer stations at hypocentral distances lesser than 150 km. We implement a mixed-effects regression analysis to explore the variability of the ground motion in terms of RSA predicted at different sites by considering two different group levels: in the first, each station is considered separately; in the second, we consider the Eurocode 8 (EC8, 2004) soil classification. Considering a probabilistic alert decision module applied to data from two selected stations, we show that the predictive models including site effects provide more reliable alerts, reducing the false alarms from 2.6% to 0.53% and the missed alarms from 10.1% to 4.8%. The residuals analysis shows that including a site-specific random effect in the predictive model contributes to reducing the apparent aleatory variability, whereas grouping data by EC8 classification does not provide significant benefit for EEW purposes.

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Language(s): eng - English
 Dates: 2020-05-052020
 Publication Status: Finally published
 Pages: 16
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1785/0120190272
GFZPOF: p3 PT4 Natural Hazards
OATYPE: Green Open Access
 Degree: -

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Title: Bulletin of the Seismological Society of America
Source Genre: Journal, SCI, Scopus
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Pages: 16 Volume / Issue: 110 (3) Sequence Number: - Start / End Page: 1289 - 1304 Identifier: CoNE: https://gfzpublic.gfz-potsdam.de/cone/journals/resource/journals59
Publisher: Seismological Society of America (SSA)