GNSS-based detection if small-magnitude earthquakes (M < 5) in Eastern Europe

Savchuk, S, Doskich, S
Space Sci. & Technol. 2025, 31 ;(2):32-41
https://doi.org/10.15407/knit2025.02.032
Publication Language: English
Abstract: 
This study explores the use of GNSS (Global Navigation Satellite Systems) for earthquake detection, employing the GipsyX software to analyze ground displacements. GNSS technology provides real-time, high-precision measurements of the Earth's crust, which are crucial for detecting the subtle shifts indicative of seismic activity. We investigated three seismic events from 2023 and 2024, assessing how the earthquake magnitude and the distance from the epicentre impacted the GNSS station coordinate time series. Stations located closer to the epicentres showed more pronounced displacements, especially in events with higher magnitudes. For smaller earthquakes, shifts were observed mainly in the vertical (Up) direction, while larger tremors caused noticeable changes in both horizontal and vertical coordinates. The results reveal that even moderate seismic activity can be detected when stations are positioned within a certain range from the epicentre. 
       These findings underscore the reliability of GNSS-based systems in monitoring seismic shifts and highlight the value of distance and magnitude in determining the extent of ground displacements. By enhancing real-time monitoring, this approach improves early warning systems, offering critical insights for earthquake preparedness and risk mitigation.
Keywords: earthquake detection, GipsyX, GNSS, high-frequency GNSS data, seismic monitoring
References: 
  1. Bertiger W., Bar-Sever Y., Dorsey A., Haines B., Harvey N., Hemberger D., ... & Willis P. (2020). GipsyX/RTGx, a new tool set for space geodetic operations and research. Advances in Space Research, 66(3), 469-489.
  2. Birinci S., Sogukkuyu F., & Saka M. H. (2024). Assessing seismic displacements and early warning using kinematic PPP with MADOCA real-time products: A case study of 2023 Kahramanmaraş (Türkiye) earthquakes. Measurement, 230, 114527.
  3. Brusak I., Tretyak K., &Pronyshyn R. (2022, October). Preliminary studies of seismicity caused by the water level changes in Dnister upper reservoir. In International Conference of Young Professionals «GeoTerrace-2022» (Vol. 2022, No. 1, pp. 1-5). European Association of Geoscientists & Engineers.
  4. Colosimo G., Crespi M., & Mazzoni A. (2011). Real-time GPS seismology with a stand-alone receiver. Geophysical Research Letters.
  5. Dittmann T., Hodgkinson K., Morton J., Mencin D., & Mattioli G. S. (2022). Comparing sensitivities of geodetic processing methods for rapid earthquake magnitude estimation. Seismological Research Letters. https://doi.org/10.1785/0220210265.
  6. Doskich S., Savchuk S., & Dzhuman B. (2023). Determination of horizontal deformation of the Earth`s crust on the territory of Ukraine based on GNSS measurements. Geodynamics, 2(35), 89–98.
  7. Häberling S. (2015). Theoretical and practical aspects of high-rate GNSS geodetic observations. ETH 194. https://doi.org/10.3929/ethz-a-010592866.
  8. Kiran S. S., Vagdevi N., Devarao M. V., Babu M. K., & Swamy K. N. (2022, January). Predicting Earthquake by using GPS Seismology and GNSS Based System. In 2022 International Conference on Computing, Communication and Power Technology (IC3P) (pp. 199-203). IEEE.
  9. Kudłacik I., Kapłon J., & Kazmierski K., et al. (2023). First feasibility demonstration of GNSS-seismology for anthropogenic earthquakes detection. Scientific Reports, 13, 20905. https://doi.org/10.1038/s41598-023-47964-2.
  10. Kumar R., Mittal S., & Sharma B. (2022). Earthquake genesis and earthquake early warning systems: challenges and a way forward. Surveys in Geophysics, 43(4), 1143–1168. https://doi.org/10.1007/s10712-022-09710-7.
  11. Lackowski M., Kaźmierski K., & Kudłacik I. (2023). Using GNSS Phase Observation Residuals and Wavelet Analysis to Detect Earthquakes. Artificial Satellites, 58(4), 341-354. https://doi.org/10.2478/arsa-2023-0014.
  12. Larson K. M., Bodin P., & Gomberg J. (2003). Using 1-Hz GPS data to measure deformations caused by the Denali fault earthquake. Science, 300(5624), 1421-1424.
  13. Li W., et al. (2022). A study on small magnitude seismic phase identification using 1D deep residual neural network. Artificial Intelligence in Geosciences. https://doi.org/10.1016/j.aiig.2022.10.002.
  14. Lin J.-T., Melgar D., Sahakian V. J., Thomas A. M., & Searcy J. (2023). Real-time fault tracking and ground motion prediction for large earthquakes with HR-GNSS and deep learning. Journal of Geophysical Research: Solid Earth, 128, e2023JB027255.
  15. Meng S., et al. (2022). Characteristics and identification method of natural and mine earthquakes: A case study on the hegang mining area. Minerals, 12(10), 1256. https://doi.org/10.3390/min12101256.
  16. Quinteros-Cartaya C., et al. (2023). Exploring a CNN Model for Earthquake Magnitude Estimation using HR-GNSS data. Journal of South American Earth Sciences. https://doi.org/10.1016/j.jsames.2024.104815.
  17. Shu Y., Shi Y., Xu P., Niu X., & Liu J. (2017). Error analysis of high-rate GNSS precise point positioning for seismic wave measurement. Advances in Space Research, 59, 2691–2713. https://doi.org/10.1016/j.asr.2017.02.006.
  18. Springer Handbook of Global Navigation Satellite Systems / Teunissen P. (Editor), Montenbruck O. (Editor). Springer, 2017.
  19. Tao Z., Li M., Sui Q., et al. (2024). Study on the correlation between real-time GNSS landslide acceleration monitoring and earthquake response: a case of May 2, 2023, MW = 5.2 Baoshanearthquake, Yunnan. Geoenvironmental Disasters, 11, 9. https://doi.org/10.1186/s40677-024-00273-w.
  20. Xu P., et al. (2013). High-rate precise point positioning (PPP) to measure seismic wave motions: An experimental comparison of GPS PPP with inertial measurement units. Journal of Geodesy, 87, 361–372. https://doi.org/10.1007/s00190-012-0606-z.