Instantaneous tracking of earthquake growth with elastogravity signals

• Published in: Nature (London) Vol. 606; no. 7913; pp. 319 - 324
• Main Authors: Licciardi, Andrea, Bletery, Quentin, Rouet-Leduc, Bertrand, Ampuero, Jean-Paul, Juhel, Kévin
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 09.06.2022; Nature Publishing Group UK
• Subjects: Algorithms; Broadband; Data Management; Databases, Factual; Deep learning; Early warning systems; Earth Sciences; Earthquake magnitude; Earthquakes; Emergency communications systems; Emergency warning programs; Geodesy; Geophysics; GEOSCIENCES; Latency; Machine learning; natural hazards; Noise; Real time; Sciences of the Universe; Seismic activity; Seismic waves; Seismograms; seismology; Seismometers; Shaking; Time functions; Tsunamis; Warning systems; Waveforms; Japan
• ISSN: 0028-0836; 1476-4687
• DOI: 10.1038/s41586-022-04672-7

Rapid and reliable estimation of large earthquake magnitude (above 8) is key to mitigating the risks associated with strong shaking and tsunamis . Standard early warning systems based on seismic waves fail to rapidly estimate the size of such large earthquakes . Geodesy-based approaches provide better estimations, but are also subject to large uncertainties and latency associated with the slowness of seismic waves. Recently discovered speed-of-light prompt elastogravity signals (PEGS) have raised hopes that these limitations may be overcome , but have not been tested for operational early warning. Here we show that PEGS can be used in real time to track earthquake growth instantaneously after the event reaches a certain magnitude. We develop a deep learning model that leverages the information carried by PEGS recorded by regional broadband seismometers in Japan before the arrival of seismic waves. After training on a database of synthetic waveforms augmented with empirical noise, we show that the algorithm can instantaneously track an earthquake source time function on real data. Our model unlocks 'true real-time' access to the rupture evolution of large earthquakes using a portion of seismograms that is routinely treated as noise, and can be immediately transformative for tsunami early warning.