Rea, Raffaele (2024) Impact-Based Earthquake Early Warning methods: developments and applications. [Tesi di dottorato]

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Abstract

Earthquakes are one of the most destructive natural phenomena, every year thousands of people suffer the consequences of these events. Even if these events are not predictable nowadays, there is something we can do to mitigate the damages produced by them. Earthquake Early Warning Systems (EEWS) have the purpose of issuing alerts of the ground shaking coming from an earthquake. They identify an ongoing seismic event and provide as soon as possible alerts to targets that will be affected by the consequences of strong ground shaking. Among the different types of systems, we focused our analysis on impact-based EEWS. We tested the method QuakeUp, proposed by Zollo et al. (2023), on different real and simulated events, assessing the performance of the system and its capacity to issue effective alerts. Analyzing the performance of the system on the 2023 Mw7.8 Turkey-Syria earthquake we find out that the system seems to be capable of tracking in real-time the evolution of the rupture process of an earthquake using a dense sensor network, in addition to the issuing of the alerts. We also compared QuakeUp to the Japanese system PLUM (Propagation of Local Undamped Motion, Hoshiba 2021) on the 2016 Mjma6.6 Central Tottori earthquake, quatifying the lead-times provided by the two systems and comparing the real-time shakemaps produced. We found that even if QuakeUp can be faster in issuing the alerts, PLUM is more accurate. We also tested the possibility of using QuakeUp on coastal areas, integrating the system to a Tsunami Early Warning Systems. Testing the method on the 2020 Mw7.0 Samos earthquake and on 150 simulations of the 1908 Mw7.0 Messina earthquake, we found that the system could be effective in tsunamigenic events. In fact, even if the seismic networks in coastal areas usually have huge azimuthal gaps, the system can provide effective seismic alerts to the cities on the coasts and can have stable source characteristics estimations in less than a minute, allowing fast tsunami alerts based on them.

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