Earthquake and tsunami prediction enhanced by deep learning model

A deep learning model developed at Los Alamos National Laboratory can help estimate earthquake magnitude in real time and provide early warnings of tsunamis.

A team of scientists at Los Alamos National Laboratory is working on a solution to predict natural disasters, starting with earthquakes and tsunamis. To achieve this, they developed a deep learning model capable of capturing the gravitational waves generated by an earthquake, and thus anticipating the risks of a tsunami.

“Our model opens an estimate of earthquake magnitude in real time, using data that is routinely treated as noise, and can be immediately transformative for tsunami early warning,” said Bertrand Roet Leduc, a scientist with the Geophysics Group at Los Alamos.

Rapid and reliable estimation of the magnitude of large earthquakes is critical to mitigating risks associated with strong shaking and tsunamis.

Currently, the systems used to detect earthquakes rely on seismic waves, which makes them unable to quickly estimate the magnitude of large earthquakes. Since the estimates are based on the shaking from the earthquake as it occurs, they cannot prevent the consequences of the disaster until it is already too late.

Moreover, standard systems cannot distinguish between earthquakes of magnitude 8 and 9, although the latter are 30 times more active and destructive. Even GPS-based methods for earthquake magnitude estimation are often subject to significant uncertainties and latency issues.

In contrast to earthquake-based early warning, the Rouet-Leduc team’s ‘fast elasto-gravitational signals’ (PEGS) light-speed approach relies on gravitational waves. This new method is not saturated with scale and can immediately distinguish between earthquakes of magnitude 8 and 9. In fact, the PEGS approach becomes more accurate the larger the earthquake. This method has not been previously tested for earthquake early warning.

The Los Alamos research team has shown that PEGS can be used in real time to track the growth and strength of earthquakes once they reach a certain magnitude. The team developed a deep-learning model that takes advantage of information carried by PEGS, which is recorded by Japan’s regional broadband seismographs.

After training the deep learning model on a database of artificial waveforms enhanced by noise measured on the seismic network, the team was able to demonstrate the first example of real-time tracking of an earthquake source on real data.

This model, combined with real-time data, can alert communities very early on if a subduction megatremor is large enough to trigger a tsunami that rips through seawalls, potentially saving thousands of lives in the process.

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