Gravity signals and artificial intelligence can quickly detect large earthquakes

Massive earthquakes don’t just move the Earth – they make adjustments in the speed of light to Earth’s gravitational field. Now, researchers have trained computers to identify these microgravity signals, showing how the signals can be used to determine the location and magnitude of a strong earthquake almost instantaneously.

This is the first step in creating a very early warning system for the planet’s most powerful earthquakes, scientists reported May 11 in Nature.

Such a system could help solve a thorny problem in seismology: how to determine the true size of a massive earthquake once it strikes, says Andrea Lisiardi, a geophysicist at the University of Côte d’Azur in Nice, France. Without this ability, it would be very difficult to issue hazard warnings quickly and effectively which could save lives.

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As large earthquakes rupture, shaking and trembling send seismic waves across the Earth that appear as large vibrations on seismographs. But current detection methods based on seismic waves are known to have difficulty distinguishing between, say, a 7.5 and 9 earthquake in the few seconds after such an event.

That’s because initial estimates of the magnitude are based on the height of seismic waves called P waves, which are the first to arrive at monitoring stations. However, for the strongest earthquakes, the initial P wave amplitudes reach a maximum, making it difficult to distinguish earthquakes of different magnitudes.

But seismic waves are not the first signs of an earthquake. All this mass moving in a large earthquake also changes the density of the rocks in different locations. These shifts in intensity translate into subtle changes in Earth’s gravitational field, resulting in “elastic gravitational” waves that travel through Earth at the speed of light – even faster than seismic waves.

Such signals were once thought to be too small to be detected, says seismologist Martin Valli of the Institute of Physics of the World in Paris, who was not involved in the new study. Then in 2017, Valley and colleagues were the first to report seeing gravitational-elastic signals in seismic station data. These results prove that “you have a window between the onset of the earthquake and the time you receive [seismic] waves,” Valley says.

But researchers are still thinking about how to turn these elastic gravity cues into an effective early warning system. Since the gravitational oscillations are small, it is difficult to distinguish them from background noise in the seismic data. When scientists looked retrospectively, they found that only six massive earthquakes in the past 30 years had generated identifiable elastic-gravity signals, including the 9-magnitude Tōhoku-Oki earthquake in 2011 that triggered a devastating tsunami that submerged two nuclear power plants in Fukushima, Japan (SN: 3/16/11). (The initial AP-wave-based estimate of the magnitude of this earthquake was 7.9).

This is where computers can come in, says Licciardi. He and his colleagues created PEGSNet, a machine learning network designed to identify “instant ElastoGravity signals.” The researchers trained the machines on a set of real seismic data collected in Japan and 500,000 simulated earthquake gravity signals in the same region. Licciardi says synthetic gravimetric data is essential for training, because real data is very scarce, and a machine learning model requires enough input to be able to find patterns in the data.

Once trained, the computers were then tested: tracing the origin and evolution of the 2011 Tohoku earthquake as if it were happening in real time. Lisiardi says the result has been promising. The algorithm was able to accurately determine the magnitude and location of the earthquake five to 10 seconds earlier than other methods.

Licciardi says this study is a proof of concept and hopefully the basis for an early warning system prototype. “Right now, it’s designed to work … in Japan. We want to build something that can work in other regions” known for strong earthquakes, including Chile and Alaska. Ultimately, the hope is to build a single system that can work globally.

The results show that PEGSNet has the potential to be a powerful tool for early earthquake warnings, particularly when used in conjunction with other earthquake detection tools, Valley says.

There is still more work to be done. For one thing, the algorithm has been trained to look for a single point of earthquake origin, which is a reasonable approximation if you’re far away. But up close, the earthquake’s origin no longer looks like a point, it’s actually a larger area that ruptured. If scientists want an accurate estimate of where the rupture will occur in the future, the machines need to look for regions, not points, Valley adds.

Greater advances could occur in the future as researchers develop much more sensitive instruments that can detect even the smallest seismic disturbances in the Earth’s gravitational field while filtering out other sources of background noise that might obscure the signals. Earth is a very noisy environment, says Valley, from its oceans to its atmosphere.

“It’s a bit like the challenge physicists face when trying to observe gravitational waves,” says Valley. These ripples in space-time, caused by massive cosmic collisions, are an entirely different kind of gravitational-driven wave (SN: 2/11/16). But gravitational wave signals are also dampened by Earth’s noise – in this case, Earth’s microtremors.

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