Earth Sciences

Instantaneous tracking of earthquake growth with elastogravity signals

A. Licciardi, Q. Bletery, et al.

This groundbreaking research by Andrea Licciardi, Quentin Bletery, Bertrand Rouet-Leduc, Jean-Paul Ampuero, and Kévin Juhel introduces prompt elastogravity signals (PEGS) and a deep learning model, PEGSNet, to revolutionize real-time earthquake magnitude estimation for large earthquakes. Discover how this innovation enhances tsunami early warning capabilities by tracking earthquake growth before seismic waves arrive.

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Index

Abstract

This paper explores the use of prompt elastogravity signals (PEGS) for real-time earthquake magnitude estimation, particularly for large earthquakes (M>8) where traditional seismic-based early warning systems often fail. The authors develop a deep learning model, PEGSNet, trained on synthetic PEGS waveforms augmented with empirical noise, to track earthquake growth instantaneously from data recorded before the arrival of seismic waves. Results show PEGSNet accurately tracks earthquake moment release for M<sub>w</sub> > 8.6, providing a significant advancement in tsunami early warning capabilities.

Publisher

Nature

Published On

Jun 09, 2022

Authors

Andrea Licciardi, Quentin Bletery, Bertrand Rouet-Leduc, Jean-Paul Ampuero, Kévin Juhel

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