Hey You! Yes, You! Do you know that you’re currently helping in producing 120 trillion GB of data every single day? Do you know that you’re helping to shape the future of our world? Yes, you are. What Machine Learning is to Data, “This” is to You! Producing something meaningful for the society that will benefit to the masses in general!
More than half a million people die every year due to natural disasters. More than 10% of it being earthquake exterminations, so shouldn’t we stop listening and start doing something about it? Well, guess what? Someone already started to study and have come to a rigid research and thesis that will help people aware about Earthquakes beforehand using nothing but what is used in your everyday phone usage! Isn’t it amazing?
This is the first time that machine learning has been used to analyze acoustic data to predict when an earthquake will occur.
The crew, from the University of Cambridge, the Los Alamos National Laboratory and the 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.
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The machine learning algorithm was able to identify a particular pattern in the sound, previously thought to be nothing more than noise, which occurs long before an earthquake. The characteristics of this sound pattern can be used to give a precise estimate (within a few percent) of the stress on the fault (that is, how much force is it under) and to estimate the time remaining before failure, which gets more and more precise as failure approaches. The team now thinks that this sound pattern is a direct measure of the elastic energy that is in the system at a given time as by Cambridge University.
“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, whose main area of research is energy-efficient and cost-effective LEDs. Humphreys was Rouet-Leduc’s supervisor when he was a Ph.D. student at Cambridge.
Although the researchers caution that there are multiple differences between a lab-based experiment and a real earthquake, they hope to progressively scale up their approach by applying it to real systems which most resemble their lab system. One such site is in California along the San Andreas Fault, where characteristic small repeating earthquakes are similar to those in the lab-based earthquake simulator. Progress is also being made on the Cascadia fault in the Pacific Northwest of the United States and British Columbia, Canada, where repeating slow earthquakes that occur over weeks or months are also very similar to laboratory earthquakes.
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