Their results were good and could be also applied to prediction of avalanches and landslides. The laboratory apparatus uses steel blocks to closely mimic the physical forces at work in a real earthquake, and also records the seismic signals and sounds that are emitted. The machine learning algorithm was able to identify a particular pattern in the sound which occurs long before an earthquake. The characteristics of this sound pattern can be used to give a precise estimate of the time remaining before failure.
“This is the first time that machine learning has been used to analyse acoustic data to predict when an earthquake will occur, long before it does, so that plenty of warning time can be given – it’s incredible what machine learning can do,” said co-author Professor Sir Colin Humphreys of Cambridge’s Department of Materials Science & Metallurgy. Multiple differences between a lab-based experiment and a real earthquake are estimated to be but the research is important for the advance of prediction methods in the real life.