Shazam for earthquakes will transform how seismologists detect tremors

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.”

Image courtesy of Ilissa Ocko

Image courtesy of Ilissa Ocko

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.”


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Skydio unveils its obstacle-dodging, thrill-seeking, AI-powered drone

An autonomous drone startup founded by former MIT researchers has today launched its R1, a fully autonomous flying camera that follows its subjects through dense and challenging environments.

In a promotional video, launched to introduce the autonomous camera, R1 can be seen following an athlete as she parkours her way through dense woodland.

The drone’s makers Skydio have explained that the camera combines artificial intelligence, computer vision, and advanced robotics and works by anticipating how people move, so R1 can make intelligent decisions about how to get the smoothest, most cinematic footage in real-time.

“The promise of the self-flying camera has captured people’s imaginations, but today’s drones still need to be flown manually for them to be useful,” said Adam Bry, CEO and co-founder of Skydio.

“We’ve spent the last four years solving the hard problems in robotics and AI necessary to make fully autonomous flight possible. We’re incredibly excited about the creative possibilities with R1, and we also believe that this technology will enable many of the most valuable drone applications for consumers and businesses over the coming years.”

Launching today is the Frontier Edition of R1, which is aimed at athletes, adventurers, and creators.

This version of R1 is powered by the Skydio Autonomy Engine, enabling it to see and understand the world around it so that it can fly safely at speeds of upto 25mph while avoiding obstacles.

The autonomous drone is fitted with 13 cameras, which gives it the ability to map and understand the world in real-time, allowing it to be fully autonomous and independently capture footage that in Skydio’s words “once required a Hollywood film crew” and will “enable a new type of visual storytelling”.

The R1 “Frontier Edition” is available for order now on Skydio’s website for $2,499.