First implementation of a tsunami warning system based on prompt elastogravity signals in Peru

SUMMARY Tsunami warning systems implemented worldwide rely on the fast characterization of earthquake sources, in particular on the estimation of the moment magnitude $\textit {M}_{\textrm {w}}$. Reliable estimation of $\textit {M}_{\textrm {w}}$ typically takes 10 to 20 min for large events based on conventional seismic signals. A promising alternative is the use of prompt elasto-gravity signals (PEGS), which are very low-amplitude gravitational perturbations induced by earthquakes that travel at the speed of light and can be recorded by broad-band seismometers at timescales of a few minutes after origin time. We propose here a first implementation of real-time PEGS analysis to enhance the Peruvian earthquake monitoring system by enabling rapid magnitude estimation for large and potentially tsunamigenic earthquakes. We train a graph neural network to recognize the space-time structure of PEGS over a large international set of broad-band seismic stations, even when their amplitudes are smaller than the noise level, and to rapidly estimate the magnitude and location of the source. Our results indicate that the PEGS-based system can estimate the magnitude of $\textit {M}_{\textrm {w}} \ge 8.2$ earthquakes, within 5 min after the event’s initiation, with sufficient accuracy for tsunami warning purposes. Simulated real-time tests confirm the viability of the PEGS-based approach for operational early warning, providing robust source estimations of large magnitude events to the Peruvian earthquake monitoring system that are valuable for tsunami warning.