After a big earthquake hits, the danger isn’t over. Smaller, follow-up quakes that are triggered by the initial shock can rumble around an affected area for months, toppling structures weakened by the parent quake. Scientists can predict the size and timing of these aftershocks to some degree, but nailing the location has always proved challenging. New research from scientists at Harvard and Google suggests AI might be able to help.
 
In a paper published in the journal Nature this week, researchers show how deep learning can help predict aftershock locations more reliably than existing models. Scientists trained a neural network to look for patterns in a database of more than 131,000 “mainshock-aftershock” events, before testing its predictions on a database of 30,000 similar pairs.
 
The deep learning network was significantly more reliable than the most useful existing model, known as “Coulomb failure stress change.” On a scale of accuracy running from 0 to 1 — in which 1 is a perfectly accurate model and 0.5 is as good as flipping a coin — the existing Coulomb model scored 0.583, while the new AI system hit 0.849.
 
Brendan Meade, a professor of Earth and planetary sciences at Harvard who helped author the paper, told ScienceDaily that the results were promising. “There are three things you want to know about earthquakes,” said Meade. “When they are going to occur, how big they’re going to be and where they’re going to be. Prior to this work we had empirical laws for when they would occur and how big they were going to be, and now we’re working the third leg, where they might occur.”
 
The success of artificial intelligence in this domain is thanks to one of the technology’s core strengths: its ability to uncover previously overlooked patterns in complex datasets. This is especially relevant in seismology, where it can be incredibly difficult to see connections in the data. Seismic events involve too many variables, from the makeup of the ground in different areas to the types of interactions between seismic plates to the ways energy propagates in waves through the Earth. Making sense of it all is incredibly hard.
 
The researchers say their deep learning model was able to make its predictions by considering a factor known as the “von Mises yield criterion,” a complex calculation used to predict when materials will begin to break under stress. As Meade tells ScienceDaily, this factor is often used in fields like metallurgy, “but has never been popular in earthquake science.” Now, with the findings of this new model, geologists can investigate its relevance.
 
Despite the success of this research, it’s far from ready to deploy in the real world. For a start, the AI model only focuses on aftershocks caused by permanent changes to the ground, known as static stress. But follow-up quakes can also be caused by rumblings in the ground that occur later, known as dynamic stress. The existing model is also too slow to work in real-time. This is important, as most aftershocks occur on the first day after a quake occurs, before roughly halving in frequency on each following day.
 
As Phoebe DeVries, a Harvard postdoc who helped lead the research, told ScienceDaily: “We’re still a long way from actually being able to forecast [aftershocks] but I think machine learning has huge potential here.”