CANADA: An algorithm inspired by a popular song-matching app is helping Stanford scientists find previously overlooked earthquakes in large databases of ground motion measurements.
They call their algorithm Fingerprint And Similarity Thresholding, or FAST, and it could transform how seismologists detect microquakes – temblors that don’t pack enough punch to register as earthquakes when analyzed by conventional methods. While microquakes don’t threaten buildings or people, monitoring them could help scientists predict how frequently, and where, larger quakes are likely to occur.
“In the past decade or so, one of the major trends in seismology has been the use of waveform similarity to find weakly recorded earthquakes,” said Greg Beroza, a professor of geophysics at Stanford School of Earth, Energy & Environmental Sciences.
The technique most commonly employed to do this, called template matching, functions by comparing an earthquake’s seismic wave pattern against previously recorded wave signatures in a database. The downsides of template matching are that it can be time-consuming and that it requires seismologists to have a clear idea of the signal they are looking for ahead of time.
The FAST technique, which is detailed in the current issue of the journal Science Advances, circumvents both of these shortcomings by taking all of the recorded data from a seismic station and chopping the continuous signal into segments of a few seconds each. The signals are then compressed into compact representations, or “fingerprints,” for rapid processing.
