One of my gateways into geology and planetary science was a fascination with earthquakes. As a new resident of California, I was rather fascinated by these events, especially after experiencing a 7.2 my first year here. I’ve always followed the field rather closely, and now, a new study in Nature led by Andrea Licciardi has potentially found a better way to detect earthquakes.

A team of researchers has used a machine-learning algorithm to analyze Global Navigation Satellite System data and find what they refer to as prompt elastogravity signals or PEGS. These are gravitational perturbations that occur during large earthquakes where massive amounts of rock are shifted. And they propagate out at the speed of light, which makes them faster to detect than regular seismic waves.

I want to make it clear that no one is using this data to predict an earthquake. The goal here is to detect quakes as soon as they happen but before the effects are felt by people in order to give communities warning time to prepare for shaking and follow-on effects like tsunamis.

And this model had a response time of about a minute. That means that the magnitude of a massive quake could be determined before the initial shaking has even stopped.

Now, according to the research, this only works on quakes above magnitude 8.3. That may sound like the usefulness is limited, but keep in mind that these immense quakes are the ones that cause the most devastating damage. If we can send out a warning tens of seconds or even minutes earlier than we can now, we could potentially save thousands of lives.

That’s always a worthwhile goal.

More Information

Monitoring Earthquakes at the Speed of Light (Eos)

“Instantaneous tracking of earthquake growth with elastogravity signals,” Andrea Licciardi et al., 2022 May 11, Nature