An earthquake-detecting algorithm based on song-matching app Shazam is helping Stanford scientists pinpoint the location of microquakes.
The idea for Fingerprint and Similarity Thresholding (FAST) occurred to Greg Beroza, professor of geophysics at Stanford School of Earth, Energy & Environmental Sciences, several years ago.
Like thousands of users before him, Beroza used Shazam to identify a song he didn’t know. Unlike thousands of other users however, Beroza realised from that initial usage that Shazam wasn’t simply comparing the digital file of the song against other files in a database. It was doing something more sophisticated, namely capturing the audio waveform of a short section of the song and comparing that snippet to other waveforms housed on an online server, while at the same time filtering out irrelevant noise from the environment such as people’s conversations.
“I thought, ‘That’s cool,’ and then a moment later, ‘That’s what I want to do with seismology,'” said Beroza.
So far the device has been able to identify several dozen weak earthquakes, but its creators’ hope that by gaining an understanding of how often different magnitudes of earthquakes happen seismologists might be able to predict how frequently large, natural quakes will occur.
In the new study, the Stanford scientists used FAST to analyse a week’s worth of data collected in 2011 by a seismic station on the Calaveras Fault in California’s Bay Area. This same fault recently ruptured and set off a sequence of hundreds of small quakes.
Not only did FAST detect the known earthquakes, it also discovered several dozen weak quakes that had previously been overlooked.
“A lot of the newer earthquakes that we found were magnitude 1 or below, so that tells us our technique is really sensitive,” said study co-author Clara Yoon. “FAST was able to spot the missed quakes because it looks for similar wave patterns across the seismic data, regardless of their energy level.”
The FAST technique could replace traditional methods of detecting microquakes, such as template matching which functions by comparing an earthquake’s seismic wave pattern against previously recorded wave signatures in a database.
Unlike template matching, FAST doesn’t require seismologists to have a clear idea of the signal they are looking for ahead of time. It works, like Shazam, by searching for similarities in pre-recorded data from a seismic shift.
“Instead of comparing a signal to every other signal in the database, most of which are noise and not associated with any earthquakes at all, FAST compares like with like,” said Beroza. “Tests we have done on a 6-month data set show that FAST finds matches about 3,000 times faster than conventional techniques. Larger data sets should show an even greater advantage.”