In May 1976, a devastating earthquake of magnitude Ms 6.5 occurred in Friuli, Italy, resulting in 976 deaths, 2000 injured, and 60,000 homeless. It is notable that, at the time of the earthquake, only one station was installed in the affected region. The resulting lack of information, combined with a dearth of mitigation planning for responding to such events, lead to a clear picture of the impact of the disaster being available only after a few days. This region is now covered by nearly 100 seismological and strong-motion stations operating in real time. Furthermore, 30 average-cost strong-motion stations have been recently added, with the goals of improving the density of real-time ground-motion observations and measuring the level of shaking recorded at selected buildings. The final goal is to allow rapid impact estimations to be made to improve the response of civil protection authorities. Today, considering the higher density seismological net- work, new efforts in terms of the implementation and testing of earthquake early warning systems as a possible tool for mitigating seismic risk are certainly worthwhile. In this article, we show the results obtained by analyzing in playback and using an algorithm for decentralized onsite earthquake early warning, broadband synthetic strong-motion data calculated at 18 of the stations installed in the region, while con- sidering the magnitude and location of the 1976 Friuli earthquake. The analysis shows that the anisotropy of the lead times is related not only to the finite nature of the source but also to the slip distribution. A reduction of 10% of injured persons appears to be possible if appropriate mitigating actions are employed, such as the development of efficient automatic procedures that improve the safety of strategic industrial facilities.

Could a Decentralized Onsite Earthquake Early Warning System Help in Mitigating Seismic Risk in Northeastern Italy? The Case of the 1976 Ms 6.5 Friuli Earthquake

Parolai S.;
2020-01-01

Abstract

In May 1976, a devastating earthquake of magnitude Ms 6.5 occurred in Friuli, Italy, resulting in 976 deaths, 2000 injured, and 60,000 homeless. It is notable that, at the time of the earthquake, only one station was installed in the affected region. The resulting lack of information, combined with a dearth of mitigation planning for responding to such events, lead to a clear picture of the impact of the disaster being available only after a few days. This region is now covered by nearly 100 seismological and strong-motion stations operating in real time. Furthermore, 30 average-cost strong-motion stations have been recently added, with the goals of improving the density of real-time ground-motion observations and measuring the level of shaking recorded at selected buildings. The final goal is to allow rapid impact estimations to be made to improve the response of civil protection authorities. Today, considering the higher density seismological net- work, new efforts in terms of the implementation and testing of earthquake early warning systems as a possible tool for mitigating seismic risk are certainly worthwhile. In this article, we show the results obtained by analyzing in playback and using an algorithm for decentralized onsite earthquake early warning, broadband synthetic strong-motion data calculated at 18 of the stations installed in the region, while con- sidering the magnitude and location of the 1976 Friuli earthquake. The analysis shows that the anisotropy of the lead times is related not only to the finite nature of the source but also to the slip distribution. A reduction of 10% of injured persons appears to be possible if appropriate mitigating actions are employed, such as the development of efficient automatic procedures that improve the safety of strategic industrial facilities.
2020
12-ago-2020
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https://pubs.geoscienceworld.org/ssa/srl/article/91/6/3323/589669/Could-a-Decentralized-Onsite-Earthquake-Early
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3037720
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