Scientists at the University of Alaska Fairbanks have unveiled a groundbreaking method to identify various types of aircraft by analyzing seismic data typically used for earthquake detection. This innovative approach utilizes the subtle ground vibrations caused by the sound waves produced by aircraft flying at high altitudes.
The research, published on November 18 in The Seismic Record, demonstrates that the frequency imprint of an aircraft, such as a Cessna 185 Skywagon, can be discerned from seismic recordings. By examining the frequency patterns within a seismic spectrogram, researchers can match these patterns to a catalog of known aircraft frequencies.
Understanding Seismometers and Aircraft Identification
Graduate student researcher Bella Seppi, who led the study, explained that aircraft signals are typically of a much higher frequency than other signals often detected by seismometers. Earthquake vibrations, for example, generally fall within lower frequency ranges, making it relatively easy to identify aircraft signals against this backdrop.
Seismometers record ground motion induced by sound waves, capturing the vibrations caused by an aircraft’s noise. These vibrations are displayed in a spectrogram, showcasing how frequencies shift as the aircraft approaches or departs from the seismometer. This phenomenon is akin to the Doppler effect, similar to the changing pitch of an approaching ambulance.
Gathering Data and Building a Frequency Catalog
The data for Seppi’s research was collected from nearly 1,200 recordings made over a span of 35 days, utilizing 303 seismometers strategically placed along the Parks Highway in Alaska. These sensors, which were originally installed to monitor aftershocks from the 2018 magnitude 7.1 Anchorage earthquake, are capable of detecting a broader range of frequencies due to their high sampling rate of 500 per second.
To accurately identify aircraft types, Seppi faced the challenge of creating a frequency catalog for different aircraft models, as no comprehensive database existed. She sourced flight data from the Flightradar24 website, which provides details about in-flight aircraft, including type, location, and flight path. By correlating this data with seismic recordings, Seppi was able to isolate the Doppler curves of each aircraft’s sound waves.
Through mathematical techniques, she removed the Doppler effect to reveal the true frequency patterns of the aircraft, forming a frequency comb that includes both the base frequency and its harmonics. The results were categorized by aircraft types, such as piston, turboprop, and jet.
The consistency of the frequency signals surprised Seppi, highlighting the potential for this method to serve various applications, including environmental assessments regarding aircraft noise over sensitive areas.
Future Directions and Applications
With this technique, researchers can develop a frequency comb from any seismic recording of an aircraft, paving the way for future comparisons against a growing catalog of known aircraft frequencies. This advancement could enhance the ability to discern not only the type of aircraft but also gather additional data about its speed and trajectory.
Further research will investigate the distance from which an aircraft can be detected and how multiple seismometers can work together to provide more comprehensive flight information. The study was co-authored by Carl Tape, a professor of geophysics at the UAF Geophysical Institute, and David Fee, a research professor also affiliated with the Geophysical Institute.
This innovative approach to aircraft identification represents a significant leap forward in utilizing seismic technology beyond its traditional applications, opening up new avenues for research and practical use in aviation monitoring.
