Assessment of geodynamic risk in the Tashkent Region using GNSS data and spatial analysis methods

DOI: 10.35595/2414-9179-2025-1-31-508-518

View or download the article (Rus)

About the Authors

Mirshodjon D. Makhmudov

Ulugh Beg Astronomical Institute of Uzbekistan Academy of Sciences,
33, Astronomicheskaya str., Tashkent, 100052, Republic of Uzbekistan,
E-mail: makhmudov0907@gmail.com

Dilbarkhon Sh. Fazilova

Ulugh Beg Astronomical Institute of Uzbekistan Academy of Sciences,
33, Astronomicheskaya str., Tashkent, 100052, Republic of Uzbekistan,

Tashkent State Technical University Named after Islam Karimov,
2, Universitetskaya str., Tashkent, 100095, Republic of Uzbekistan,

E-mail: dil_faz@yahoo.com

Khasan N. Magdiev

Cadaster agency under Economy and Financial Ministry of Republic Uzbekistan,
6V, Chuponata str., Tashkent, 100097, Republic of Uzbekistan,

Ulugh Beg Astronomical Institute of Uzbekistan Academy of Sciences,
33, Astronomicheskaya str., Tashkent, 100052, Republic of Uzbekistan,

E-mail: hasan.magdiev@gmail.com

Ikhtiyar M. Ergeshov

Cadaster agency under Economy and Financial Ministry of Republic Uzbekistan,
6V, Chuponata str., Tashkent, 100097, Republic of Uzbekistan,
E-mail: ergeshovikhtiyar75@gmail.com

Nurmukhammad M. Mukhtorov

Ulugh Beg Astronomical Institute of Uzbekistan Academy of Sciences,
33, Astronomicheskaya str., Tashkent, 100052, Republic of Uzbekistan,
E-mail: mnurmukhammad@gmail.com

Abstract

The assessment of contemporary surface deformations and their spatial distribution is crucial for territorial planning, monitoring, and risk management in geodynamically active regions. The Tashkent Region is characterized by a combination of complex fault structures, high population density, and intensive industrial development, which determines the necessity of a comprehensive analysis of geodynamic processes using modern GIS technologies. This study aimed to map and spatially zone the intensity of contemporary deformation processes using continuous GNSS station data from 2018 to 2024. Spline interpolation based on GNSS data was applied as the main spatial analysis method, enabling the construction of a reliable deformation intensity field and the identification of key zones within the Tashkent Region. As a result, areas with maximum deformation intensities of up to 10 nanostrains per year (nstrain/y) were identified, which is more than three times the regional average (2–4 nstrain/y). The highest values were observed along major fault zones as well as in industrial centers (Angren, Almalyk), where up to 25 earthquakes with magnitudes of 2.5–4.0 were recorded in recent years. The zoning map was classified by risk levels, allowing for the identification of areas with increased geodynamic activity and potential threats to infrastructure and the population. Comparison with seismic data for 2018–2024 confirmed that the areas with high deformation intensity also correspond to zones with the highest density of seismic events. The resulting maps were integrated into a GIS environment and can support decision-making in risk management, monitoring system development, and territorial planning. The novelty of this study lies in the integration of modern satellite-based GNSS data with GIS tools to provide a comprehensive quantitative assessment of regional geodynamic activity and to produce informative maps for practical territorial management.

Keywords

GNSS, deformation intensity zoning, Tashkent Region, GIS spatial analysis, spline interpolation

References

  1. Alavi S.H., Bahrami A., Mashayekhi M., Zolfaghari M. Optimizing Interpolation Methods and Point Distances for Accurate Earthquake Hazard Mapping. Buildings, 2024. V. 14. No. 6. Art. 1823. DOI: 10.3390/buildings14061823.
  2. Altamimi Z., Rebischung P., Métivier L., Collilieux X. ITRF2014: A New Release of the International Terrestrial Reference Frame Modeling Nonlinear Station Motions. Journal of Geophysical Research: Solid Earth, 2016. V. 121. No. 8. P. 6109–6131. DOI: 10.1002/2016jb013098.
  3. Araszkiewicz A. Integration of Distributed Dense Polish GNSS Data for Monitoring the Low Deformation Rates of Earth’s Crust. Remote Sensing, 2023. V. 15. Art. 1504. DOI: 10.3390/rs15061504.
  4. Atabekov I.U., Sadikov Yu.M. Stress State of the Earth’s Crust in the Western Tien Shan in Central Asia (Uzbekistan): A Mathematical Stress Model. Geotectonics, 2022. No. 3. P. 50–65 (in Russian).
  5. Bahadır M., Ocak F., Şen H. Determination of the Development of Settlements Above Earthquake Susceptibility Classes in Atakum District (Samsun/Türkiye). International Journal of Engineering and Geosciences, 2024. V. 9. No. 3. P. 390–405. DOI: 10.26833/ijeg.1465072.
  6. Baldina E.A., Lebedeva E.V., Medvedev A.A. Technique for Interpretation of Archive and Recent Satellite Images to Study the Slope Processes Dynamics in the Geyzernaya River Valley (Kamchatka). InterCarto. InterGIS. Proceedings of International Conference, 2022. V. 28. Part 1. P. 266–283 (in Russian). DOI: 10.35595/2414-9179-2022-1-28-266-283.
  7. Bande A.E. The Tectonic Evolution of the Western Tien Shan, Potsdam, August 2016. Web resource: https://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-398933 (accessed 01.07.2024)
  8. Banica A., Rosu L., Muntele I., Grozavu A. Towards Urban Resilience: A Multi-Criteria Analysis of Seismic Vulnerability in Iasi City (Romania). Sustainability, 2017. V. 9. No. 2. Art. 270. DOI: 10.3390/su9020270.
  9. Brunet M.-F., McCann T., Sobel E.R. Geological Evolution of Central Asian Basins and the Western Tien Shan Range. London: Geological Society, 2017. DOI: 10.1144/SP427.
  10. Dong D., Herring T.A., King R.W. Estimating Regional Deformation from a Combination of Space and Terrestrial Geodetic Data. Journal of Geodesy, 1998. V. 72. P. 200–214. DOI: 10.1007/s001900050161.
  11. Fazilova D., Makhmudov M., Khalimov B. The Analysis of Crustal Deformation Patterns in the Tashkent Region, Uzbekistan, Derived from GNSS Data Over the Period 2018–2023. Geodesy and Geodynamics, 2025. V. 16. No. 2. P. 137–146. DOI: 10.1016/j.geog.2024.07.001.
  12. Fazilova D., Makhmudov M., Magdiev K. Analysis of Crustal Movements in the Angren-Almalyk Mining Industrial Area Using GNSS Data. International Journal of Geoinformatics, 2023. V. 19. No. 11. P. 12–19. DOI: 10.52939/ijg.v19i11.2915.
  13. Hao M., Li Y., Zhuang W. Crustal Movement and Strain Distribution in East Asia Revealed by GPS Observations. Scientific Reports, 2019. V. 9. Art. 16797. DOI: 10.1038/s41598-019-53306-y.
  14. Herring T.A., King R.W., Floyd M., McClusky S.C. Introduction to GAMIT/GLOBK. Release 10.7. Technical report. Massachusetts Institute of Technology, 2018. Web resource: http://geoweb.mit.edu/gg/Intro_GG.pdf (accessed 10.09.2022).
  15. Hervas D., Carracedo P., Franco G. Spatial Interpolation Model with Covariates Using Thin Plate Splines. Decision Sciences. DSA ISC 2024. Lecture Notes in Computer Science, 2025. V. 14778. DOI: 10.1007/978-3-031-78238-1_25.
  16. Hussain E., Gunawan E., Hanifa N.R., Zahro Q. The Seismic Hazard from the Lembang Fault, Indonesia, Derived from InSAR and GNSS Data. Natural Hazards and Earth System Sciences, 2023. V. 23. No. 10. P. 3185–3197. DOI: 10.5194/nhess-23-3185-2023.
  17. IERS Conventions. IERS Technical Note 36. Frankfurt am Main: Verlag des Bundesamts für Kartographie und Geodäsie, 2010. 179 p.
  18. Kuzmin Yu.O. Paradoxes of Comparative Analysis of Measurements by Methods of Ground and Satellite Geodesy in Modern Geodynamics. Izvestiya, Physics of the Solid Earth, 2017. No. 6. P. 24–39 (in Russian). DOI: 10.7868/S0002333717060023.
  19. Makhmudov M.D., Fazilova D.Sh. Construction the Velocity Field in a Regular Grid in the Tashkent Region on the Basis Interpolation of GNSS Permanent Stations Data. InterCarto. InterGIS. Proceedings of the International Scientific Conference. Moscow: Lomonosov Moscow State University, Faculty of Geography, 2023. V. 29. Part 1. P. 535–545 (in Russian). DOI: 10.35595/2414-9179-2023-1-29-535-545.
  20. Mavlyanova N.G., Ibragimov R.S., Ibragimova T.L., Rakhmatullaev K.K. Features of Seismogravitational Processes in Zones of Active Earthquake Manifestation in Central Asia (A Case Study of Uzbekistan). Environmental Geoscience, 2021. No. 2. P. 27–40 (in Russian). DOI: 10.31857/S0869780921020053.
  21. Rahmadani S., Meilano I., Susilo S., Sarsito D.A., Abidin H.Z., Supendi P. Geodetic Observation of Strain Accumulation in the Banda Arc Region. Geomatics, Natural Hazards and Risk, 2022. V. 13. No. 1. P. 2579–2596. DOI: 10.1080/19475705.2022.2126799.
  22. Shen Z.K., Jackson D.D. Crustal Deformation Across and Beyond the Los Angeles Basin from Geodetic Measurements. Journal of Geophysical Research: Solid Earth, 1996. V. 101. P. 27957–27980. DOI: 10.1029/96JB02544.
  23. Shen Z.K., Wang M., Zeng Y., Wang F. Optimal Interpolation of Spatially Discretized Geodetic Data. Bulletin of the Seismological Society of America, 2015. V. 105. No. 4. P. 2117–2127. DOI: 10.1785/0120140247.
  24. Thammaboribal P., Tripathi N.K., Lipiloet S. Pre-Seismic Signature Detection using Diurnal GPS-TEC and Kriging Interpolation Maps (ASK-VTEC Technique): 11 May 2011, M9.0 Tohoku Earthquake Case Study. International Journal of Geoinformatics, 2024. V. 20. No. 11. P. 148–161. DOI: 10.52939/ijg.v20i11.3715.
  25. Tsay O.G. Electronic Map of Faults of the Middle, Southern Tien Shan and Adjacent Territories. Proceedings of the LII Tectonic Meeting “Fundamental Problems of Tectonics and Geodynamics”, 2019. V. 2. P. 382–386. Web resource: http://www.ginras.ru/materials/files/MTS-2020-2%20.pdf (accessed 01.10.2024) (in Russian).
  26. Zhao Q., Ding K., Lan G., Wu Y., Liu Y., Peng S., Li T. Spatiotemporal Characteristics of Horizontal Crustal Deformation in the Sichuan-Yunnan Region Using GPS Data. Remote Sensing, 2023. V. 15. Art. 4724. DOI: 10.3390/rs15194724.

For citation: Makhmudov M.D., Fazilova D.Sh., Magdiev K.N., Ergeshov I.M., Mukhtorov N.M. Assessment of geodynamic risk in the Tashkent Region using GNSS data and spatial analysis methods. InterCarto. InterGIS. Moscow: MSU, Faculty of Geography, 2025. V. 31. Part 1. P. 508–518. DOI: 10.35595/2414-9179-2025-1-31-508-518 (in Russian)