Gipronickel Institute, Saint-Petersburg, Russia

A. E. Rumyantsev, Head of Geotechnical Engineering Laboratory, Candidate of Engineering Sciences, RumyantsevAE@nornik.ru
D. V. Vorobiev, Engineer Category III of Geotechnical Engineering Laboratory

Center for Geodynamic Safety, Nornickel’s Polar Division, Norilsk, Russia
A. K. Ustinov, Head of Department for Geotechnical Supervision of Mining Practice
A. V. Kalyakina, Chief Specialist, Department of Continuous Monitoring and Rock Pressure

When dealing with problems of geotechnology, it is nec essary to select a method for processing structural data in conformity with such criteria as unambiguous interpretation of the result, minimum subjectivity inflicted by a specialist, and data processing speed. The article presents a cluster analysis algorithm for structural data of geotechnical oriented borehole drilling. Clustering allows structuring and interpreting complex and manydimensional data, which is particularly important when studyi ng nonuniform natural systems. These systems have a complex spatial geometry describable by azimuth and dip angle among other things. The analysis of such data needs methods capable to reveal hidden laws and to generate uniform groups of data for the further examination. Clustering involves two stages. The first stage is preliminary processing of data, including a dimensional scaling procedure and standardization. Reduction of values of attributes to a uniform scale is a necessary condition of the correction calculation of distances in a manydimensional attribute space. The second stage is clustering using k-means. The latter is selected because of its efficiency in handling a large bulk of data. The k-means minimizes intracluster dispersion, and ensures compactedness and discriminability of distinguished groups. Furthermore, k-means can point at the natural groups of objects. Statistical processing of structural data, in particular, the clustering analysis, enables sound decision-making on clustering of oriented structures or systems of cracks, makes it possible to decrease the degree of the human subjectivity in detection of systems of cracks, and provides repeatability of the process at different stages of such data analysis, which is important in geotechnical research.

1. Zubov V. P., Trofimov A. V., Kolganov A. V. Influence of ground control features on indicators of dilution in mines of the Talanakh ore province. MIAB. 2024. No. 12-1. pp. 87–106.
2. Zakharov V. N., Klebanov D. A., Makeev M. A., Radchenko D. N. Analysis of methods to prepare and transform information entering data repositories for effective management of the mining system. Gornaya Promyshlennost. 2023. No. 5S. pp. 10–17.
3. Sabyanin G. V., Balandin V. V., Trofimov A. V., Kuzmin S. V. Geomechanical survey procedure for Oktyabrsky mine. Gornyi Zhurnal. 2020. No. 6. pp. 11–16.
4. Meng F., Wong L. N. Y., Zhou H., Wang Z., Zhang L. Asperity degradation characteristics of soft rock-like fractures under shearing based on acoustic emission monitoring. Engineering Geology. 2020. Vol. 266. ID 105392.
5. Darbinyan T. P., Tsymbalov A. A., Zubov V. P., Kolganov A. V. Impact of rock mass jointing on dilution of disseminated copper–nickel ore in Oktyabrsky Mine. Gornyi Zhurnal. 2023. No. 6. pp. 19–25.
6. Kongar-Syuryun Ch. B., Kovalski E. R. Hardening backfill at potash mines: Promising materials regulating stress-strain behavior of rock mass. Geologiya i Geofizika Yuga Rossii. 2023. Vol. 13, No. 4. pp. 177–187.
7. Pleshko M. S., Pankratenko A. N., Pleshko M. V., Nasonov A. A. Assessment of stress–strain behavior of shaft lining in bottomhole area during sinking by real-time monitoring and computer modeling data. Eurasian Mining. 2021. No. 1. pp. 25–30.
8. Kaplunov D. R., Aynbinder I. I., Fedotenko V. S., Yukov V. A. Underground ore mining technologies: Current challenges, sustainable development and transition to a new technological paradigm. Gornyi Zhurnal. 2021. No. 9. pp. 4–11.
9. Korchak S. A., Abaturova I. V., Savintsev I. A., Storozhenko L. A. Rock mass quality assessment to reveal potentially hazardous areas in open pit mine design. MIAB. 2022. No. 9. pp. 87–98.
10. Abaturova I. V., Storozhenko L. A., Pisetsky V. B., Savintsev I. A. Use of geological and structural analysis in evaluating engineering and geological conditions of mineral deposits. Engineering and Mining Geophysics : Proceedings of the 16th Conference and Exhibition. Houten : European Association of Geoscientists and Engineers, 2020. Vol. 2020. DOI: 10.3997/2214–4609.202051096
11. Miftakhov R. F., Avdeev P. A., Gogonenkov G. N., Bazanov A. K., Efremov I. I. Mapping of faults based on machine learning and neural networks. Geologiya Nefti i Gaza. 2021. No. 3. pp. 123–136.
12. Fedotov G. S., Sapronova N. P. Geological and mining information systems as a tool for digital transformation of production processes in mining companies. Marksheyderiya i nedropolzovanie. 2021. No. 4(114). pp. 54–59.
13. Zuev B. Yu. Methodology of modeling nonlinear geomechanical processes in blocky and layered rock masses on models made of equivalent materials. Journal of Mining Institute. 2021. Vol. 250. pp. 542–552.
14. Abdrakhmanov M. I., Lapin S. E., Shnayder I. V. Clustering algorithms in express-analysis of seismic data. MIAB. 2019. No. 6. pp. 27–44.
15. Arthur D., Vassilvitskii S. K-means++: The advantages of careful seeding. SODA ‘07: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia : Society for Industrial and Applied Mathematics, 2007. pp. 1027–1035.
16. Raschka S., Mirjalili V. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2. 3^rd ed. Packt, 2019. 770 p.
17. Vakhromeeva E. N., Zenzinova Yu. B. Automation of company clustering by financial metrics using the k-means algorithm on big data. Diskussiya. 2024. No. 5(126). pp. 46–50.
18. Lourenço A., Bulò S. R., Rebagliati N., Fred A. L. N., Figueiredo M. A. T. et al. Probabilistic consensus clustering using evidence accumulation. Machine Learning. 2013. Vol. 98, Iss. 1–2. pp. 331–357.
19. Miraftabzadeh S. M., Colombo C. G., Longo M., Foiadelli F. et al. K-means and alternative clustering methods in modern power systems. IEEE Access. 2023. Vol. 11. pp. 119596–119633.
20. Hu H., Liu J., Zhang X., Fang M. An effective and adaptable k-means algorithm for big data cluster analysis. Pattern Recognition. 2023. Vol. 139. ID 109404.