Two new papers demonstrate analytical tools that greatly enhance scientists’ ability to interpret low-amplitude seismic signals…though to very different ends. One shows how to efficiently tease signals of very low-energy events out of the overwhelming flood of data pouring out of seismometric stations around the world. The other shows how weather tilts the land below it, offering a tool for tracking small thermal vortices—dust devils—here and on Mars.
For most of us, seismology means the Big Ones, significant earthquakes and wild jumps of the seismograph. Those spikes are reflected numerically in the ratios of short-term and long-term average ground motions (STA/LTA) that pass a threshold value.
This doesn’t work quite as well for subtler events: those masked by noise, overlapping signals, long-developing signals, and the rumbles and thumps of human activity. Many seismic phenomena, such as the slipping of a particular fault at a particular place, recur over periods ranging from weeks to years, generating a characteristic seismic-wave signature each time. To find miniquakes that haven’t stepped over the detector threshold, seismologists use a template matching technique, reducing the known quake signature to a “correlation coefficient” and then searching the geological database for a match.
Template matching, however, requires a signature for a known event. Finding unknown leitmotifs in the flood of seismic data is more challenging. In its most extreme form, it requires comparing every 10-second snippet of data to every other 10-second snippet of data in the terabyte deluge from the world’s seismographs stations (totaling some 21,899, according to the International Seismological Centre registry).
That fine-toothed-comb-level matching, called autocorrelation, has up until now been the most accurate way of doing these comparisons, consumes a great deal of time and computing power. In a test, an autocorrelation analysis of just one week’s worth of data from a single station near San Jose, Calif., took 9 days and 13 hours.
To speed things up, researchers at Stanford University’s Department of Geophysics and Institute of Computational and Mathematical Engineering, have taken a cue from apps like Shazam, which help users identify snatches of melody that they just can’t put a name to. These programs identify the main characteristics of a complex wave spectrum, compress them, and represent the result as an ordered series of values—a vector “fingerprint.” The fingerprint is used to assign the snippet to a particular bin (locality sensitive hashing). This operation is repeated until the entire data set has been fingerprinted and categorized. The analysts then compare each snippet to all of the other snippets in that bin; when the fingerprints agree and the source wave-forms match, the two events can be identified as originating from the same sort of tectonic movement.
Lead Stanford author Clara Yoon and her colleagues built their Fingerprint and Similarity Thresholding (FAST) system not on Shazam’s analytical engine, but on Google’s WavePrint fingerprinting package (originally developed for processing images). The team started with the same week-long data file they used for the autocorrelation test. This time, they filtered the signal to concentrate on the 4-to-10 Hertz band (the earthquake’s voice range) and compressed it from 100 samples per second to 20. They then broke the whole dataset into tens of thousands of overlapping 10-second segments, reduced each segment to a 4096-bit binary fingerprint, and assigned it to a bin.
Confining the final time-consuming autocorrelation comparison to a few closely related segments in the same bin slashes computer run-time. The same analysis that required 9.5 days of autocorrelation analysis took 96 minutes of FAST processing. Both the autocorrelation and the FAST analyses detected about the same number of events (86 and 89, respectively), and each of them found 43 previously unreported events. FAST did have a weakness: autocorrelation correctly identified all 24 of the previously reported events, while FAST missed 3. Then again, FAST located 25 events that standard template matching missed (versus 19 for autocorrelation).
FAST’s run-time advantage becomes more marked as the amount of data increases. Analyzing 6 months of seismometer data would take FAST a couple of days. Autocorrelating the same data would eat up about 20 years.
The Dust Devil is in the Details
Elsewhere, an international team has figured out how to use seismometer data to identify and track dust devils, the swirling vortices that seem to come out of nowhere to lift sand and debris into a wild dance. They look like small tornadoes, but they are, in fact, a sort of anti-tornado. Tornadoes draw their energy from a storm system, and are usually associated with thunder, clouds, and rain. Dust devils are born from hot air rising from the surface, in weather that is clear, blazing, and dry.
But, like tornadoes, dust devils form around a core of low pressure. And, in addition to sucking in surrounding air to make a spinning funnel, the reduction in air pressure also reduces the load on the ground beneath. The ground responds by rising up, creating a detectable tilt that extends out beyond the wall of the vortex.
A group of researchers (from Johns Hopkins University Applied Physics Lab in Laurel, Md., the Jet Propulsion Laboratory in Pasadena, Calif., the Institut Supérieur de l'Aéronautique et de l'Espace in Toulouse, France, and the Institut de Physique du Globe de Paris) found that the 1 to 2 millibar pressure drop in typical a 5-meter-diameter dust devil would reduce downward force on the ground by the equivalent of 810 kilograms (just a little less than the curb weight of a Mitsubishi Mirage , cited by Motor Trend as perhaps the lightest vehicle now sold in the U.S.). The pressure drop for a really big dust devil, on the other hand, could amount to some 300 tons. Their work is reported in Bulletin of the Seismological Society of America (with open access on arXiv).
In an experiment on a dry lake bed in the California desert (on a site operated by JPL and “within sight of the 70-meter Deep Space Network antenna”), Ralph Lorenz of Johns Hopkins and his collaborators confirmed their calculations using a network of eight pressure loggers set up along the 60-meter arms of a cross centered on an existing seismic station. They matched the seismic and barometric data, and found that the seismograph did indeed record the earth tilting up toward the center of a dust devil. The degree of tilt, moreover, corresponded to the size and distance of the vortex…and the seismograph traces of acceleration in the north-south and east-west directions showed the direction of the wind’s closest approach to the center of the cross.
The objective of this research is not so much directed at happenings on Earth, but those on Mars, where dust devils are can often be seen crossing the red deserts in caravans. (See Lorentz’s blog post on Martian dust devils on the Planetary Society website.) In Mars’s rarer atmosphere, a 0.1-millibar pressure drop in a 15-meter-wide dust devil would produce about the same Mitsubishi Mirage-sized decrease in ground force as a typical California devil. Thirty meters away, the ground would tilt by about 5 x 10-9 radians, enough to register on the seismometers planned for NASA’s Mars InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) mission, scheduled to launch in March 2016.