Artificial intelligence algorithm predicts slow earthquakes

Upload your content

A team of researchers from the French École Normale Supérieure (ENS) recently announced the discovery of an AI algorithm to predict seismic events.


This latest research project adopted a simple approach that involved feeding a deep learning machine with large amounts of seismic data recorded before quakes occurred. The goal was to develop an algorithm with the ability to detect recurrent phenomena that lead up to a significant seismic event.

The machine was initially trained using laboratory earthquakes, before being adapted to look for signs of “slow” earthquakes under Vancouver Island, Canada. Seismic events of this kind, which share many characteristics with more devastating fast earthquakes, can last for a period of weeks or even months. Given that they are slow, they hardly generate any seismic waves and as a result do not usually cause much in the way of damage. At the same time, the fact that they take place over a long period makes them easier to analyze.

Promising results

The algorithm created a list of characteristics that precede the appearance of earthquakes. Among them, the researchers noted an exponential increase in seismic energy prior to rupture, as if more and more tiny seismic waves were being emitted from the seismic zone. These crackles were noticeable up to three months before a slow earthquake would usually be detected, which means that the advent of these phenomena could be predicted well in advance.


What about detecting major earthquakes?

As it stands, there has been little progress towards this goal.


Hugely destructive earthquakes are also relatively rare, which means that researchers do not have enough seismic data to train an AI to predict them. Further study of the mechanics of seismic events so that they can be modeled by computers and eventually predicted appears to be the most promising research strategy for the moment.

Explore further

Hazards Earthquake
Share this

Please note: Content is displayed as last posted by a PreventionWeb community member or editor. The views expressed therein are not necessarily those of UNDRR, PreventionWeb, or its sponsors. See our terms of use

Is this page useful?

Yes No
Report an issue on this page

Thank you. If you have 2 minutes, we would benefit from additional feedback (link opens in a new window).