Massive earthquakes don’t just move the ground — they make speed-of-light adjustments to Earth’s gravitational field.
As large earthquakes rupture, the shaking and shuddering sends seismic waves through the ground that appear as large wiggles on seismometers. But current seismic wave–based detection methods notoriously have difficulty distinguishing between, say, a magnitude 7.5 and magnitude 9 quake in the few seconds following such an event.
Such signals were once thought to be too tiny to detect, says seismologist Martin Vallée of the Institut de Physique du Globe de Paris, who was not involved in the new study. Then in 2017, Vallée and his colleagues werein seismic station data. Those findings proved that “you have a window in between the start of the earthquake and the time at which you receive the [seismic] waves,” Vallée says.
That’s where computers can come in, Licciardi says. He and his colleagues created PEGSNet, a machine learning network designed to identify “Prompt ElastoGravity Signals.” The researchers trained the machines on a combination of real seismic data collected in Japan and 500,000 simulated gravity signals for earthquakes in the same region.
The results show that PEGSNet has the potential to be a powerful tool for early earthquake warnings, particularly when used alongside other earthquake-detection tools, Vallée says.
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