Scientists suspect that there is a relationship between earthquakes that begin deep inside the earth and the total electron content over a one square meter column in the ionosphere, some fifty to a few hundred kilometres above the earth’s surface. Studies suggest that changes in electron content can be detected even before an earthquake. So monitoring the electron count could be useful in predicting earthquakes. However, besides earthquakes, the electron content can vary due to solar activity and geomagnetic storms. There are also diurnal variations in the electron content.
So how can we overcome these confounding factors to identify precursors to earthquakes from electron content in the ionosphere?
Researchers from the Seismo-electromagnetics and Space Research Laboratory, Agra started investigating the 2023 Turkiye earthquake sequence. On February 6 that year, an earthquake of magnitude 7.8 on the moment magnitude scale shook Turkiye. Two major aftershocks followed that day.
To take the confounding variables into consideration , from the OMNIWeb and the Kyoto World Data Center, the researchers extracted the F10.7 index, a measure the 10.7 centimeter radio wavelength emitted by the sun, a precursor to solar activity. They also extracted data on the Kp index which announces the arrival of solar wind, and the Dst index, a proxy of magnetic storms on earth.
The researchers extracted data on the electron count from four global navigation satellite system stations whose satellites send radio signals to a ground station. The time delay and phase changes between two different frequencies in a signal is used to calculate electron content in the ionosphere. Thus, the researchers obtained measures of the electron count with a 30-second temporal resolution.
Raw total electron content data contain measurement imperfections. So, the researchers cleaned the data on the vertical total electron count using GPS-TEC software. They converted slant to a vertical electron count of a square metre, retaining data of satellite elevations above 30 degrees to reduce errors of low-elevation observations.
To define an objective anomaly criterion for the electron count, the researchers used a moving-window interquartile-range method. Each window covered thirty minutes of observations containing about sixty measurements. By arranging the measurements in ascending order, they found exact middle observations. They separated the data into four quarters. The difference between the last value in the first and the third quarters were calculated, and values beyond one and a half times the interquartile range were defined as anomalies.
To provide a broad background for comparison, the researchers analysed the ionospheric electron count data from January 7 to March 8. Between January 22 and 26, 10 to 15 days before the earthquake, the researchers found significant anomalies.
Was it the earthquake that produced these anomalies? What is the mechanism that produces such anomalies?
The researchers focused on short atmospheric oscillations especially in the frequency range between one and ten megahertz, typical of earthquake-related ionospheric oscillations. They analysed the frequency and timing of the signals using continuous wavelet transform, a mathematical signal-processing tool which breaks down a time-domain signal into various wavelets. Wavelets show frequency changes with time – useful to detect transient signals.
The researchers calculated wavelet power spectra and identified the first significant post-earthquake signals. The response appeared within about thirty minutes of the earthquake. Dominant frequencies ranged from 1.31 to 2.07 megahertz. All four stations showed this response. The researchers measured the arrival time of the response at each station. Corresponding periods ranged from about 200 to 500 seconds. The researchers describe this as a rapid ionospheric response to the earthquake.
Using this data, they calculated propagation velocities, using earthquake-to-ionospheric pierce point distances and arrival delays. The velocities ranged from 464 to 2212 metres per second. These values match acoustic and seismo-acoustic propagation.
The team also examined the response across stations and found that distance alone did not control amplitude. More distant stations sometimes showed stronger disturbances.
The researchers then examined the pre-seismic changes in the electrons in the ionosphere. They compared the timing, frequency, and propagation to understand the physical mechanism that produces the anomalies. Both pre-seismic and post-seismic ionospheric disturbances involve the coupling of the lithosphere and ionosphere via the intervening atmosphere. But they are governed by different mechanisms.
The pre-seismic anomalies are initiated by seismogenic electric fields. These fields ionise the atmosphere. Stress accumulation in tectonic plates adds to that ionization. This build up happens a few days before the earthquake. The post-seismic signatures, on the other hand, are due to the upward propagation of earthquake-generated acoustic and seismo-acoustic waves. Ground motion generates atmospheric acoustic disturbances. These waves travel upward into the ionosphere and produce travelling ionospheric disturbances.
Records of total electron count in the one square metre column of the ionosphere can thus act as pre- and post- earthquake signatures.
‘The electron count alone cannot predict earthquakes reliably. Multiple independent observations must support any precursor’, says Birbal Singh, Seismo-electromagnetics and Space Research Laboratory, Agra.
But this is just the beginning. Scientists are accumulating other clues that help predict earthquakes more accurately. Wait for the follow-up report in the near future.
Journal of Atmospheric and Solar–Terrestrial Physics, 286: 106925 (2026);
DOI: 10.1016/j.jastp.2026.106925
Reported by Ravindra Jadav
Associate Professor, Government Science College, Santrampur
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