Kunaev Institute of Mining, Almaty, Kazakhstan

Bimurat Zh., Researcher, PhD
Makhmetova G. N., Research Assistant, Post-Graduate Student, makhgulmira@mail.ru
Baltiyeva A. A., Head of Rock Pressure Laboratory, Post-Graduate Student

Satbayev University, Almaty, Kazakhstan

Sagindykov B. Zh., Associate Professor, Candidate of Physical and Mathematical Sciences

The flooding of the Mirgalimsay Mine in the 1990s triggered the activation of hazardous geological processes in the adjacent area, including the city of Kentau. Subsidence, sinkholes and alterations in the hydrogeological regime are observed, posing significant risks to people and infrastructure. Consequently, monitoring of ground surface deformations using modern remote sensing techniques, such as Interferometric Synthetic Aperture Radar (InSAR), has become particularly relevant. This paper presents an integrated monitoring methodology based on InSAR, including Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) techniques, which enables assessment of vertical displacements with millimeter-level accuracy while minimizing the impact of atmospheric and orbital errors. Using Kentau as a case study, we demonstrate that this approach effectively identifies zones of intensive subsidence (up to 25 mm/year), validates data through ground-based measurements, and provides recommendations for ongoing monitoring and risk mitigation.

This research is funded by the Committee of Industry of the Ministry of Industry and Construction of the Republic of Kazakhstan under program-targeted funding for scientific research for 2024–2026, BR23991563: "Creation of Innovative Resource-Saving Technologies for Mining and Integrated Processing of Mineral and Technogenic Raw Materials. We extend our sincere gratitude to N. O. Berdinova for her invaluable assistance in the material search and editing of this article.

1. Cruz H., Véstias M., Monteiro J., Neto H., Duarte R. P. A Review of Synthetic-Aperture Radar Image Formation Algorithms and Implementations: A Computational Perspective. Remote Sensing. 2022. Vol. 14, Iss. 5. ID 1258.
2. Chan Y. K., Lee Y. C., Koo V. C. Design and Implementation of Synthetic Aperture Radar (SAR) Field-Programmable Gate Array (FPGA)-Based Processor. Applied Sciences. 2022. Vol. 12, Iss. 4. ID 1808.
3. Zhu C., Long S., Zhang J., Wu W., Zhang L. Time Series MultiSensors of Interferometry Synthetic Aperture Radar for Monitoring Ground Deformation. Frontiers in Environmental Science. 2022. Vol. 10. ID 929958.
4. Ho Tong Minh D., Hanssen R. F., Doin M. P., Pathier E. Advanced Methods for Time‐series InSAR. Surface Displacement Measurement from Remote Sensing Images. 2022. Ch. 5. pp. 125–154. DOI: 10.1002/9781119986843.ch5
5. Marghany M. Synthetic Aperture Radar Image Processing Algorithms for Nonlinear Oceanic Turbulence and Front Modeling. Elsevier, 2024. 416 p. DOI: 10.1016/C2022-0-01174-0
6. Richards M. A., Melvin W. L. Principles of Modern Radar. Basic principles (Radar, Sonar and Navigation) 2nd Edition. US : Scitech Publishing, 2023. 1152 p.
7. Arenas-Pingarrón Á., Brisbourne A. M., Martín C. et al. An Alternative Representation of Synthetic Aperture Radar Images as an AID to the Interpretation of Englacial Observations. EGUsphere. 2025. DOI: 10.5194/egusphere-2025-1068
8. Wu H., Zhang Y., Kang Y. et al. SAR Interferometry on Full Scatterers: Mapping Ground Deformation with Ultra-High Density from Space. Remote Sensing of Environment. 2024. Vol. 302. ID 113965.
9. Li H., Dong J., Wang Y., Liao M. Monitoring Surface Deformation Using Distributed Scatterers InSAR. Journal of Geodesy and Geoinformation Science. 2024. Vol. 7, Iss. 1. pp. 42–58.
10. Giorgini E., Orellana F., Arratia C. et al. InSAR Monitoring Using Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) Techniques for Ground Deformation Measurement in Metropolitan Area of Concepción, Chile. Remote Sensing. 2023. Vol. 15, Iss. 24. ID 5700.

11. Bitelli G., Ferretti A., Gianicco C. et al. Subsidence Monitoring in Emilia-Romagna Region (Italy) from 2016 to 2021: From InSAR and GNSS Integration to Data Analysis. Remote Sensing. 2025. Vol. 17, Iss. 6. ID 947.
12. Mullojanova G. M., Aminzhanova M. B. Concept of Monitoring of Earth Surface Displacement and Deformation by Remote Sensing Data. Economy and Society. 2024. Vol. 121, Iss. 6-1. pp. 1230–1233.
13. Sidiq T. P., Gumilar I., Abidin H. Z. et al. Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia. Applied Sciences. 2025. Vol. 15, Iss. 7. ID 3732.
14. Shevchuk R. V., Manevich A. I., Akmatov D. Zh., Urmanov D. I., Shakirov A. I. Geodynamic Monitoring of the Ground Surface and Mining Industry Infrastructure Using Radar Interferometry. Russian Mining Industry. 2022. Vol. 5. pp. 99–104.
15. Koptyakov D. A., Masalsky N. A., Kharisov T. F., Balek A. Y. Application of Interferometry to Describe Landside Processes Associated with the Karakechinsky Deposit. Izvestija Tulskogo Gosudarstvennogo Universiteta. Nauki o Zemle. 2024. Vol. 3. pp. 376–387.
16. Zhai M., Liu Q., Tao Q., Liu G. SBAS InSAR Subsidence Monitoring for Mining Areas Based on Levelling Constraints. Journal of Physics Conference Series. 2023. Vol. 2620, Iss. 1. ID 012003.
17. Pozdnyakov A. P. Application of satellite radar interferometric synthetic aperture (InSAR) for the development of oil and gas fields. Neftegaz. 2023. Available at: https://neftegas.info/articles/article/73 (accessed: 28.08.2025).
18. Zhai G., Yang H., Shirzaei M., Manga M., Rumeng G. S54B-01 Mechanisms of induced seismicity associated with subsurface mining activities: insights from poroelastic modeling of fluID injection and extraction. AGU Fall Meeting Abstracts. 2024. Available at: https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1664762 (accessed: 28.08.2025).
19. Osmanov R. S. Application of InSAR methods for determining deformation source parameters: Integration with classical surface monitoring techniques. Proceedings of the Digital Reality Conference 2022. 2022. pp. 20–26.
20. Ge L., Chang H. -C., Rizos C. Mine Subsidence Monitoring Using Multi-source Satellite SAR Images. Photogrammetric Engineering & Remote Sensing. 2007. Vol. 73, Iss. 3. pp. 259–266.
21. Perski Z., Nescieruk P., Wojciechowski T. How to minimize the risk of landslides for hydrotechnical structures in Poland. Inżynier Budownictwa. 2021. Vol. 10. pp. 34–38.
22. Yan H., Dai W., Xu W. et al. A method for correcting InSAR interferogram errors using GNSS data and the K-means algorithm. Earth Planets Space. 2024. Vol. 76, Iss. 51. DOI: 10.1186/s40623-024-01999-5
23. Tomás R., Diaz E., Szeibert W. T. et al. Geomorphological characterization, remote sensing monitoring, and modeling of a slow-moving landslide in Alcoy (Southern Spain). Landslides. 2023. Vol. 20, Iss. 6. pp. 1293–1301.
24. ECMWF ERA5 Reanalysis. Available at: https://cds.climate.copernicus.eu/ (accessed: 28.08.2025).
25. GMTSAR. Available at: https://github.com/gmtsar/gmtsar (accessed: 28.08.2025).
26. StaMPS. Available at: https://homepages.see.leeds.ac.uk/~earahoo/stamps/ (accessed: 28.08.2025).
27. LiCSAR. Available at: https://comet.nerc.ac.uk/COMET-LiCS-portal/ (accessed: 28.08.2025).
28. Industrial Zone of Kentau Declared as an Ecological Emergency Zone until 2075. Available at: https://www.zakon.kz/pravo/6477316-promyshlennayazona-kentau-obyavlena-zonoy-chrezvychaynoy-ekologicheskoy-situatsii-do-2075-goda.html (accessed: 28.08.2025).