Khabarovsk Federal Research Center, Far Eastern Branch, Russian Academy of Sciences, Khabarovsk, Russia

I. Yu. Rasskazov, Director, Corresponding Member of the Russian Academy of Sciences, Doctor of Engineering Sciences, rasskazov@igd.khv.ru

Institute of Mining, Far Eastern Branch, Russian Academy of Sciences—Detached Division of the Khabarovsk Federal Research Center, Khabarovsk, Russia
P. A. Anikin, Leading Researcher, Candidate of Engineering Sciences
A. P. Grunin, Senior Researcher, Candidate of Engineering Sciences
A. V. Konstantinov, Researcher

In spite of the accumulated experimental and theoretical knowledge on the processes influencing the stress–strain behavior of rocks, the challenge of preventing hazardous geodynamic events in underground mining is becoming increasingly urgent. The complication of geological conditions and the growing manmade impact on the geoenvironment drive an increase in dynamic rock pressure events, which necessitates consideration of numerous factors that have effect on the geomechanical behavior of rock masses. The integrated monitoring systems installed at the Apatit, Streltsovo and Dalnegorsk deposits capture both high- and low-energy events, and ensure complete observation of geodynamic processes. This article presents the concept of development and improvement of integrated geomechanical monitoring systems. It explores the application of machine learning and cluster analysis methods for seismoacoustic data classification, provides examples of subsystem data analysis, and proposes a time series-based approach to training mathematical models to predict hazardous geodynamic events. The models developed using random forest and gradient boosting algorithms help identify complex patterns, enhancing the data processing efficiency and the reliability of rockburst predictions, which improves mining safety. Advances in geomechanical monitoring systems using cuttingedge technology offer promising opportunities for more reliable and effective prediction of hazardous geodynamic phenomena.

1. Eremenko V. A. Natural and man-caused factors of development of mining strikes in the development of iron ore deposits in Western Siberia. MIAB. 2012. No. 11. pp. 50–59.
2. Semenova I. E., Zhuravleva O. G., Zhukova S. A. Seismicity as an echo of stress–strain behavior change in rock mass in the course of mining. MIAB. 2021. No. 6. pp. 46–58.
3. Lomov М. А., Sidlyar А. V., Konstantinov А. V., Grunin А. V. Geomechanical problems of mining the lower horizons of the Yuzhnoe deposit (Primorsky Krai). MIAB. 2023. No. 12-2. pp. 87–99.
4. Małkowski P., Niedbalski Z. A comprehensive geomechanical method for the assessment of rockburst haz ards in underground mining. International Journal of Mining Science and Technology. 2020. Vol. 30, Iss. 3. pp. 345–355.
5. Аbetov А. E., Kudaibergenova S. S., Sidorov V. A. Creation of geodynamic polygons and technologies for conducting geodynamic monitoring in the hydrocarbon fields. Vestnik KazNITU. 2021. Vol. 143, No. 2. pp. 3–13.
6. Rasskazov I. Yu., Fedotova Yu. V., Anikin P. A., Migunov D. S., Konstantinov A. V. Improvement of methods and means of geomechanical monitoring based on digital technologies. Gornaya Promyshlennost. 2023. Special issue No. S5. pp. 18–24.
7. Rasskazov I. Yu., Dolgikh G. I., Petrov V. A., Lugovoi V. A., Dolgikh S. G. et al. Laser strainmeter in integrated geodynamic monitoring within Streltsov Ore Field. Journal of Mining Science. 2016. Vol. 52, No. 6. pp. 1052–1059.
8. Trifonov D., Grigoreva A., Sergushev A. Three-component strain sensor for geomechanical monitoring of rock mass. Komponenty i tekhnologii. 2018. No. 1(198). pp. 22–23.
9. Rasskazov I. Yu., Kalinov G. A., Anikin P. A. et al. Automated ground pressure control system Prognoz-ADS. Patent RF, No. 129484. Applied: 02.03.2021. Published: 25.01.2022.

10. Gladyr A. V. Integration of microseismic and geoacoustic data of geomechanical monitoring. MIAB. 2017. No. 6. pp. 220–234.
11. Galata V. Yu., Nikonorov N. V., Shvetsov V. A. System for collecting seismic activity data. Patent RF, No. 45029. Applied: 06.02.2004. Published: 10.04.2005. Bulletin No. 10.
12. Anikin P. A., Migunov D. S., Tereshkin A. A. et al. Local rockburst hazard control device Prognoz-L2. Patent RF, No. 135980. Applied: 11.07.2022. Published: 23.03.2023.
13. Zhou Z., Rui Y., Cai X., Lan R., Cheng R. A closed-form method of acoustic emission source location for velocity-free system using complete TDOA measurements. Sensors. 2020. Vol. 20, Iss. 12. ID 3553.
14. Xi J., Xiao B., Ma H., Wang Q. Research on acoustic emission source location method based on cross-correlation analysis without measuring sonic speed. Proceedings of the 26^th Chinese Control and Decision Conference. Singapore : IEEE Industrial Electronics, 2014. pp. 4392–4395.
15. Xin B., Huang Z., Huang S., Feng L. Ensemble learning improves the efficiency of microseismic signal classification in landslide seismic monitoring. Sensors. 2024. Vol. 24, Iss. 15. ID 4892.
16. Ester M., Kriegel H.-P., Sander J., Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. Portland, 1996. pp. 226–231.
17. Bychkov I. V., Vladimirov D. Ya., Oparin V. N., Potapov V. P., Shokin Yu. I. Mining information science and Big Data concept for integrated safety monitoring in subsoil management. Journal of Mining Science. 2016. Vol. 52, No. 6. pp. 1195–1209.
18. Zhou J., Zhang Y., Li C., He H., Li X. Rockburst prediction and prevention in underground space excavation. Underground Space. 2024. Vol. 14. pp. 70–98.
19. Zhang Q., Zheng T., Wang X., Fang Z. Multi-source information monitoring test of fractured rock mass destruction characteristics and sensitivity analysis of precursor phenomena. Energies. 2022. Vol. 15, Iss. 2. ID 538.
20. Stabile T. A., Fat-Helbary E. R., Serlenga V., Panebianco S., Tizzani P. et al. Fault structure and earthquake clustering in Aswan region (Egypt) revealed by high-precision earthquake location from 35 years of recorded natural and induced seismicity. Earth and Planetary Science Letters. 2024. Vol. 642. ID 118881.
21. Sharma A., Vyas S., Nayyar A. Density-based spatio-temporal clustering model for earthquake analysis and seismo-tectonic zoning. Advances in Data Science and Computing Technologies : Select Proceedings of ADSC 2022. Series: Lecture Notes in Electrical Engineering. Singapore : Springer, 2023. Vol. 1056. pp. 539–546.
22. Song Y., Wang E., Yang H., Liu C., Di Y. et al. Comprehensive early warning of rockburst hazards based on unsupervised learning. Physics of Fluids. 2024. Vol. 36, Iss. 7. DOI: 10.1063/5.0221722