Seismic sensors used to identify types of aircraft flying over Alaska
Seismological Society of America
An array of seismic sensors deployed to capture aftershocks from the 2018 magnitude 7.1 Anchorage earthquake also collected distinctive signals from hundreds of flights crossing over Alaska.
In their study published in The Seismic Record, Isabella Seppi and colleagues at the University of Alaska Fairbanks show that these signals can be used to identify the type of aircraft, along with details such as the closest time, distance and speed of each plane or helicopter as it flew above the seismic array.
Acoustic waves generated by flying aircraft vibrate the ground below, transforming sound energy into ground motion that can be detected by seismic sensors.
While previous studies have used seismometers to record aircraft, Seppi and colleagues took this research a step further by identifying specific source frequencies in the seismic data that were associated with different aircraft types.
The researchers could then distinguish frequencies generated by piston, turboprop, jet and helicopter aircraft.
“Even though we can see clear signatures of aircraft in the data, it is still amazing to realize that the acoustic waves emitted by planes from kilometers up in the sky can move the ground,” Seppi said. “And these data are good enough to determine the flight parameters and the aircraft source frequencies.”
“In one case, we could detect the subtle difference in RPM [revolutions per minute] for a propeller plane ascending on a flightseeing tour of Denali versus descending,” she added.
Todd Rust, owner of K2 Aviation in Talkeetna, Alaska, confirmed the regular changes in RPM during the particular flightseeing tour that Seppi and colleagues saw in their seismic data.
The researchers used data collected from 303 seismic sensors in February and March 2019. The sensors were placed along the Parks Highway in central Alaska, between Nenana and Talkeetna.
“We were aware that these sensors, like microphones, could pick up signals from aircraft. And we know that Alaska is an exceptionally quiet place with exceptionally interesting aircraft, so it seemed like a great realm to explore,” Seppi explained.
Seppi and colleagues estimated flight parameters for 1216 known flights from 48 aircraft types. They were able to check their estimates using flightradar 24’s ground-truth data set of flight paths in Alaska that crossed the seismic array during the two months.
Seppi said the sensors recorded with high sample rates, which allowed the researchers to capture the high frequency signals generated by the aircraft. “An optimal sensor to record an aircraft is probably one that is installed right at the ground surface. Ours were installed into frozen ground, many under the snowpack,” she noted.
Seppi and colleagues say their research could show seismologists one way to remove the contaminating data of aircraft signals from seismic data used for earthquake monitoring. Scientists could also use these data to help quantify the impacts of aircraft noise on wildlife and people in a specific region.
With a more permanent seismic network and analysis methods like machine learning, the seismic approach might be able to detect specific flight paths and aircraft type in poor weather or an acoustically “noisy” environment, the researchers write in their study.
Journal
The Seismic Record
Method of Research
Observational study
Subject of Research
Not applicable
Article Title
Classification of Aircraft Types Using Seismic Data in Alaska
Article Publication Date
18-Nov-2025
Seismic data can identify aircraft by type
University of Alaska Fairbanks
Instruments typically used to detect the ground motion of earthquakes can also be used to identify the type of aircraft flying far overhead, research by University of Alaska Fairbanks scientists shows.
That’s because aircraft sound waves also shake the ground, though to a much lesser extent.
An aircraft’s type — a Cessna 185 Skywagon, for example — can be determined by analyzing a seismic spectrogram to find the aircraft’s frequency imprint from the sound waves it creates and then matching it with data from a catalog of aircraft frequency patterns.
“Aircraft signals are a lot higher frequency than anything else that’s prominent in the spectrum that seismometers are recording,” said graduate student researcher Bella Seppi, who is leading the research. “Earthquake signals and other signals that people are typically looking for are a lot lower frequency, so aircraft signals are pretty obvious most of the time.”
The method was published Nov. 18 in The Seismic Record, journal of the Seismological Society of America.
The discovery is a breakthrough in identification of airborne aircraft. More work lies ahead, including building an aircraft frequency pattern catalog more complete than the limited one produced for the research.
Seppi said the procedure could also be used for projecting potential sound impacts of aircraft types over environmentally sensitive areas.
“This new method has many uses,” she said.
The science
Seismometers record ground motion, including vibrations caused by sound waves — also known as acoustic waves or pressure waves. They record ground vibrations caused by an aircraft’s sound.
The waves show Doppler-shifted frequencies when displayed in a spectrogram, which is created by mathematically transforming the seismic data into a display of frequency changes over time. Higher frequencies indicate an aircraft approaching a seismometer, and lower frequencies indicate one moving away.
Think of an approaching ambulance. Its sound pitch rises as it nears, then drops as it travels away. That’s the Doppler effect.
The ambulance's true pitch is what you hear the moment it passes you by.
It’s the same for aircraft. The information provided by a seismometer produces a spectrogram showing an aircraft’s changing frequencies as it moves.
Data for Seppi’s work came from nearly 1,200 recordings made over 35 days by 303 seismometers funded by the National Science Foundation. The sensors, spaced approximately 1 kilometer apart along the highway, had been installed to record aftershocks of the 2018 magnitude 7.1 Anchorage earthquake and to image the subsurface structure.
These sensors can detect a wider range of frequencies than others in the state because they have a higher sample rate — 500 per second. Alaska’s seismic stations would need to be upgraded to that rapid sampling rate to be able to identify aircraft by type.
The research
Producing a spectrogram showing the changing frequencies of a passing aircraft doesn’t by itself reveal the aircraft’s type, however.
Seppi needed to find the aircraft’s true, or base, frequency by removing the Doppler effect and then creating a “frequency comb,” which is an object’s base frequency and its related harmonics — its recurring frequency pattern.
Most objects have a base frequency and harmonics because their vibration or motion is rarely perfectly smooth.
But how would Seppi know the frequency pattern of a Cessna 185, for example?
No catalog of aircraft type frequency patterns exists, so Seppi had to build one in order to proceed.
She began by gathering data from the Flightradar24 website, which provides information about an in-flight aircraft’s type, location, altitude, speed, flight path, origin and destination.
She then matched flight times from that website with the corresponding times in the spectrogram produced from the seismic record in the study area, which consisted of a portion of the Parks Highway between Nenana and Talkeetna, Alaska, in February and March 2019.
With a match, she had the Doppler curves of each aircraft’s sound waves.
The next step was to mathematically remove the Doppler effect to get the aircraft’s true frequency pattern — its frequency comb.
With that, she began building a frequency comb catalog for aircraft traveling in the study area, grouping them by type: piston, turboprop and jet.
“What surprised me the most is how consistent a lot of the frequency signals are,” she said.
With Seppi's technique, a frequency comb can be developed from any seismic recording of an aircraft. In the future, that could be compared to a catalog of known frequency combs to identify the aircraft type. Additional information, such as direction and speed, can also be drawn from the spectrogram curves by analyzing the influence of the Doppler effect upon them.
Additional work will focus on trying to determine how far away each aircraft can be for its first detection and using data from multiple seismometers to determine additional flight information.
Co-authors include geophysics professor Carl Tape of the UAF Geophysical Institute and College of Natural Science and Mathematics and research professor David Fee, also of the Geophysical Institute.
The work was primarily funded by the U.S. Department of Defense.
CONTACTS:
• Bella Seppi, University of Alaska Fairbanks, irseppi@alaska.edu
• Carl Tape, University of Alaska Fairbanks, ctape@alaska.edu
• Rod Boyce, University of Alaska Fairbanks Geophysical Institute, 907-474-7185, rcboyce@alaska.edu
Journal
The Seismic Record
Method of Research
Meta-analysis
Article Title
Classification of Aircraft Types Using Seismic Data in Alaska
Article Publication Date
18-Nov-2025
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