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Riding the waves: Shazam for earthquakes

Riding the waves: Shazam for earthquakes cigdem
06 Feb
Valuable earthquake data can be collected from extremely weak seismic activity, but only if you can actually detect them. A new system inspired by a smartphone music app could be the answer

Perhaps you’ve seen other people doing it, or maybe you’ve even done it yourself. Either way, it isn’t unusual nowadays to see people suddenly pull out their phone and hold it outstretched when music is playing. With popular smartphone apps like Shazam able to filter out background sounds and isolate individual snippets of waveforms, our handheld technology needs only short excerpts of music to tell us exactly what song we are listening to.

‘That’s what I want to do with seismology,’ says Greg Beroza, professor of geophysics at Stanford School of Earth, Energy & Environmental Sciences. ‘I had recently given a seminar in which I lamented the fact that I couldn’t do what I really wanted to do, which was to search years of continuous waveform data to identify earthquakes because it would require too much computation.’ Beroza used the Shazam app while shopping and was impressed by how quickly it matched a song that was playing despite the fact that the environment was noisy. ‘I quickly realised that whatever sort of algorithm Shazam was using could be applied to searching for earthquakes as well,’ he says. Beroza was inspired to use this technology to develop FAST (Fingerprint And Similarity Thresholding), a new algorithm to help him and his team detect extremely small ‘microquakes’.

It doesn’t matter if one earthquake happened ten years ago and the other happened yesterday – they’re going to have waveforms that look very similar

Just as Shazam can identify entire music tracks based on tiny ‘fingerprints’ within fragments of audio waveforms, FAST filters out background noise in order to detect weak tremors in the earth, which can subsequently be properly located and characterised using existing algorithms. ‘We search for pairs of fingerprints that are similar, and then map those back to the time windows that they came from,’ says study co-author Clara Yoon, a graduate student in Beroza’s research group. ‘It doesn’t matter if one earthquake happened ten years ago and the other one happened yesterday – they’re actually going to have waveforms that look very similar.’

While microquakes aren’t themselves damaging to buildings or infrastructure, detecting them can provide valuable clues towards predicting larger, more threatening earthquakes. Previous methods of doing this were slow, and required knowing exactly what you were searching for beforehand, a process which FAST has considerably sped up. ‘Tests we have done on a six-month data set show that FAST finds matches about 3,000 times faster than conventional techniques,’ claims Beroza. ‘Larger data sets should show an even greater advantage.’

This article was published in the February 2016 edition of Geographical magazine.

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