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.
