Live-testing a light-speed gravity-based tsunami warning system

Ziel

Earthquakes caused nearly one million fatalities in the last 25 years and billions of euros of economic loss. Earthquake early warning systems exist in some countries at risk. They send alerts based on the rapid analysis of early seismic waves generated by the earthquake and therefore only provide a few seconds of warning before the strongest shakings. Moreover, for fundamental reasons, these systems systematically underestimate the magnitude of large events, which results in dramatic underestimation of potential subsequent tsunamis (which typically cause most of the fatalities and damage). For this reason, alternative systems based on different messenger-signals (W-phase, GNSS) have been developed for tsunami warning but these systems are much slower, limiting the effectiveness of protective actions. In this context, making tsunami warning faster is crucial to mitigate the hazard associated with these catastrophic events. In the framework of the ERC StG project EARLI, we designed a prototype AI algorithm leveraging recently-identified light-speed gravity signals that provides faster and more accurate theoretical estimates of the magnitude of large earthquakes than state-of-the-art tsunami warning algorithms (Licciardi et al., Nature, 2022). In this project, we propose to implement this AI algorithm in the operational tsunami warning system of Alaska, one of the regions hosting the largest earthquakes on Earth, with the objective of transforming the theoretical developments of the ERC StG EARLI into concrete transformative improvements in rapid tsunami response, which will rapidly benefit to hundreds of thousands of people at risk from this hazard in Alaska, and will serve as a proof of concept for every region exposed to tsunami hazard in the world.