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Machine learning used to predict earthquakes in a lab setting

last modified Nov 01, 2017 08:20 AM
A group of researchers from University of Cambridge, Los Alamos National Laboratory and Boston University has used machine learning techniques to successfully predict earthquakes. Although their work was performed in a laboratory setting, the experiment closely mimics real-life conditions, and the results could be used to predict the timing of a real earthquake.

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.

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 further details, please see the source article here.

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