This is the first time that machine learning has been used to analyse acoustic data to predict when an earthquake will occur.
- Colin Humphreys
The team, from the University of Cambridge, Los Alamos National Laboratory and Boston University, identified a hidden signal leading up to earthquakes and used this ‘fingerprint’ to train a machine learning algorithm to predict future earthquakes. Their results, which could also be applied to avalanches, landslides and more, are reported in the journal Geophysical Review Letters.
For geoscientists, predicting the timing and magnitude of an earthquake is a fundamental goal. Generally speaking, pinpointing where an earthquake will occur is fairly straightforward: if an earthquake has struck a particular place before, the chances are it will strike there again. The questions that have challenged scientists for decades are how to pinpoint when an earthquake will occur, and how severe it will be. Over the past 15 years, advances in instrument precision have been made, but a reliable earthquake prediction technique has not yet been developed.
As part of a project searching for ways to use machine learning techniques to make gallium nitride (GaN) LEDs more efficient, the study’s first author, Bertrand Rouet-Leduc, who was then a PhD student at Cambridge, moved to Los Alamos National Laboratory in New Mexico to start a collaboration on machine learning in materials science between Cambridge University and Los Alamos. From there the team started helping the Los Alamos Geophysics group on machine learning questions.
Image: Haiti Earthquake
Credit: United Nations Development Programme
Reproduced courtesy of the University of Cambridge