A framework to quantify the effectiveness of earthquake early warning in mitigating seismic risk

Earthquake early warning systems (EEWSs) aim to rapidly detect earthquakes and provide timely alerts, so that users can take protective actions prior to the onset of strong ground shaking. The promise and limitations of EEWSs have both been widely debated. On one hand, an operational EEWS could miti...

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Bibliographic Details
Published in:Earthquake spectra Vol. 39; no. 2; pp. 938 - 961
Main Authors: Papadopoulos, Athanasios N, Bose, Maren, Danciu, Laurentiu, Clinton, John, Wiemer, Stefan
Format: Journal Article
Language:English
Published: London, England Earthquake Engineering Research Institute 01-05-2023
SAGE Publications
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Summary:Earthquake early warning systems (EEWSs) aim to rapidly detect earthquakes and provide timely alerts, so that users can take protective actions prior to the onset of strong ground shaking. The promise and limitations of EEWSs have both been widely debated. On one hand, an operational EEWS could mitigate earthquake damage by triggering potentially cost- and life-saving actions. These range from automated system responses such as slowing down trains to the actions of individuals that receive the alerts and take protective measures. On the other hand, the effectiveness of an EEWS is conditional on the ability to issue warnings that are sufficiently accurate and timely to facilitate an appropriate action. The refinement of earthquake early warning (EEW) algorithms and the installation of denser and faster seismic networks have improved performance; however, the benefit in risk reduction that an EEWS could achieve remains unquantified. In this study, we leverage upon regional event-based probabilistic seismic risk assessment to devise a quantitative and fully customizable framework for evaluating the effectiveness of EEW in mitigating seismic risk. We demonstrate this framework using Switzerland as a testbed, for which we compute and contrast human loss exceedance curves with and without EEW.
ISSN:8755-2930
1944-8201
DOI:10.1177/87552930231153424