Assessing the efficiency of radar monitoring of pit slope stability in Kuzbass coal mines

DOI: https://doi.org/10.30686/1609-9192-2026-3-79-87

Читать на русскоя языке E.I. Shumskaya, O.V. Panina, S.G. Eremin, N.L. Krasyukova, T.V. Butova
Financial University under the Government of the Russian Federation, Moscow, Russian Federation
Russian Mining Industry №3/ 2026 p. 79-87

Abstract: Intensification of surface coal mining in the Kuznetsk Basin when the pit depths reaching 250–320 m aggravates the problem of open-pit slope stability with 12 to 18 deformation events of varying magnitudes being recorded annually. This study assesses the efficiency of ground-based interferometric radar systems (GB-InSAR) in monitoring the geomechanical slope stability of open-pit coal mines in Kuzbass. The study makes a hypothesis that the use of submillimeter-accurate radar monitoring will significantly improve the reliability of slope failure prediction compared to the traditional geodetic methods and will ensure early warning of the destructive events at least 48 hours in advance. The study was conducted using data from three open-pit coal mines of Kuzbassrazrezugol Management Company (Bachatsky, Kedrovsky, and Taldinsky) for the period of 2022–2025. The IBIS ArcSAR and IBIS-Rover radar systems with the measurement precision of ±0.1 mm and the scanning range of up to 5 km were used. The radar data were verified using GNSS observations. The methodology includes calculating cumulative displacements in the line of sight (LOS), plotting inverse strain rate (INV) graphs, and determining the stability coefficient Fs using the circular sliding surface method. During the analyzed period, the radar systems recorded 23 cases of abnormal deformation acceleration, 19 of which were confirmed by subsequent destructive events (the detection accuracy of 82.6%). The average lead time for failure prediction using the inverse strain rate method was 67.4 hours with the determination factor of R2 = 0.91–0.96. Integration of the radar monitoring into the geotechnical risk management system reduced the number of incidents related to side failures by 64% over the three years of observation. The paper describes limitations of the method under the extreme atmospheric conditions of Western Siberia, the prospects for implementing neural network forecasting models, and the integration of the Sentinel-1 satellite data for upscaling the monitoring system to a number of open-pit mines in the basin

Keywords: radar monitoring, pit slop stability, ground-based interferometric radar, inverse strain rate method, Kuzbass openpit coal mines, rock mass deformations, failure prediction

For citation: E.I. Shumskaya, O.V. Panina, S.G. Eremin, N.L. Krasyukova, T.V. Butova. Assessing the efficiency of radar monitoring of pit slope stability in Kuzbass coal mines. Russian Mining Industry. 2026;(3):79–87. (In Russ.) https://doi.org/10.30686/1609-9192- 2026-3-79-87


Article info

Received: 01.02.2026

Revised: 24.03.2026

Accepted: 06.04.2026


Information about the authors

Ekaterina I. Shumskaya – Cand. Sci. (Econ.), Associate Professor, Department of Public and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Olga V. Panina – Cand. Sci. (Econ.), Associate Professor of the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Sergey G. Eremin – Dr. Sci. (Law), Associate Professor of the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Natalya L. Krasyukova – Dr. Sci. (Econ.), Professor of the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Tatyana V. Butova – Dr. Sci. (Econ.), Associate Professor of the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.


References

1. Wu H., Zhang Y., Sun X., Liu Y., Lu Z., Kang Y. et al. A traceability investigation of the 2023 Xinjing open-pit coal mine landslide: remote sensing analysis using spaceborne SAR and optical imagery. Landslides. 2026;23(2):399–416. https://doi.org/10.1007/ s10346-025-02635-3

2. Idarmachev S.G. The system of continuous monitoring of parameters of cracks in a mountain range on the basis of resistive sensors. Geology and Geophysics of Russian South. 2025;15(1):82–91. (In Russ.) https://doi.org/10.46698/ VNC.2025.78.66.007

3. Rodionov V.A., Seregin A.S., Ikonnikov D.A. Multiplicative method to assess fire and explosion hazard of mine air containing hydrocarbon gases. Gornyi Zhurnal. 2023;(9):35–40. (In Russ.) https://doi.org/10.17580/gzh.2023.09.05

4. Carlà T., Intrieri E., Di Traglia F., Nolesini T., Gigli G., Casagli N. Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses. Landslides. 2017;14(2):517–534. https:// doi.org/10.1007/s10346-016-0731-5

5. Pisetskiy V.B., Lapin S.E. Addressing the problem of remote forecast for objects associated with the risk of the development of hazardous geodynamic phenomena during underground mining operations. Occupational Safety in Industry. 2025;(1):90– 95. (In Russ.) https://doi.org/10.24000/0409-2961-2025-1-90-95

6. Bizyaev A.A., Vostretsov A.G., Smirnyagin I.I., Sharapova M.D. Assessment of stress-strain behavior from electromagnetic radiation data in rock mass. Journal of Mining Science. 2024;60(6):1064–1070. https://doi.org/10.1134/S106273912406022X

7. Carlà T., Farina P., Intrieri E., Ketizmen H., Casagli N. Integration of ground-based radar and satellite InSAR data for the analysis of an unexpected slope failure in an open-pit mine. Engineering Geology. 2018;235:39–52. https://doi.org/10.1016/j. enggeo.2018.01.021

8. Read J., Stacey P. Guidelines for Open Pit Slope Design. Melbourne: CSIRO Publishing; 2009. 496 p. https://doi. org/10.1071/9780643101104

9. Xu X., Zhu W., Li H., Song Q., Wang Y., Gao N. Rock slope landslide prediction with an improved inverse velocity model using radar monitoring data. Engineering Geology. 2025;357:108320. https://doi.org/10.1016/j.enggeo.2025.108320

10. Shedko Yu.N., Kharchenko K.V., Zudenkova S.A., Moskvitina E.I., Babayan L.K. A synergetic approach to open-pit mine management using big data and intelligent predictive analytics systems. Russian Mining Industry. 2025;(1):154–160. (In Russ.) https://doi.org/10.30686/1609-9192-2025-1-154-160

11. Rozhdestvenskaya I.A., Belyaev A.M., Lukichev K.E., Zubenko A.V., Laffakh A.M. Development of smart distributed data storage and analysis systems for optimization of mining operations and coal mining management. Russian Mining Industry. 2025;(2):56–64. (In Russ.) https://doi.org/10.30686/1609-9192-2025-2-56-64

12. Kharchenko K.V., Zubets A.Zh., Razumova E.V., Moskvitina E.I., Voronova E.I. Integration of distributed cloud computing to improve coal mining efficiency and monitoring of mining processes. Russian Mining Industry. 2025;(2):82–90. (In Russ.) https://doi.org/10.30686/1609-9192-2025-2-82-90

13. Kharchenko K.V., Zubets A.Zh., Moskvitina E.I., Babayan L.K., Laffah A.M. Analyzing the efficiency of implementing predictive maintenance of mining equipment based on Industry 4.0 technologies. Russian Mining Industry. 2024;(4):130– 138. (In Russ.) https://doi.org/10.30686/1609-9192-2024-4-130-138

14. Novoselova I.Yu., Novoselov A.L. Alternative methods of economic assessment of environmental pollution by energy facilities. Economics, Taxes & Law. 2025;18(2):67–76. (In Russ.) https://doi.org/10.26794/1999-849X-2025-18-2-67-76

15. Samarin I.V. Application of deep learning and satellite data for monitoring and forecasting forest fires in Russia: performance and perspective analysis. Voprosy Ecologii. 2024;37(1):128–155. (In Russ.) https://doi.org/10.25726/m7116- 1845-7217-x

16. Novoselova I.Yu., Novoselov A.L. Methodological support for the coordination of projects for the socio-economic development of the region and its potentials. Economics, Taxes & Law. 2025;18(4):89–100. (In Russ.) https://doi. org/10.26794/1999-849X-2025-18-4-89-100