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MODELING OF GEOMECHANICAL PROCESSES
ArticleName Flood risk assessment in mines of the Upper Kama Potassium–Magnesium Salt Deposit: Prospects for neural network models
DOI 10.17580/gzh.2025.01.22
ArticleAuthor Losev I. V., Evseev A. V., Kamaev A. A., Zhukova I. A.
ArticleAuthorData

Geophysical Center, Russian Academy of Sciences, Moscow, Russia
I. V. Losev, Researcher, i.losev@gcras.ru

A. A. Kamaev, Laboratory Engineer

 

Mining Institute, Ural Branch, Russian Academy of Sciences, Perm, Russia
A. V. Evseev, Senior Researcher, Candidate of Engineering Sciences

 

Uralkali PJSC, Berezniki, Russia
I. A. Zhukova, Head of Department

Abstract

The Upper Kama is one of the largest deposits of potassium–magnesium salts in the world. The major problem in underground salt mining is the integrity preservation in the impermeable strata between salt strata and aquifers. Therefore, the stoping system applied at the Upper Kama deposit involves rooms and stiff pillars. The article describes the preliminary analysis of geological and geotechnical conditions as a case-study of a mine at the Upper Kama Potassium–Magnesium Salt Deposit. Formation of a source data base is discussed. Using the data base, the test calculations are performed with the limited number of data. The calculations demonstrate the promising nature of joint application of neural networks and discrete mathematical analysis. With a formed set of data, the effectiveness of identification of hazard classes is proved. Thus, it is possible to arrive at the conclusion that, on principle, the proposed technology is efficient in assessment of risk of the impermeable stratum disintegration. This approach enables correlating events, hazards and deformations in mines and on ground surface, for the flood countermeasures to be implemented promptly.
The study was supported by the Ministry of Science and Higher Education of Russia in the framework of the approved state contracts with the Geophysical Center, Reference No. FMWG-2022-0005), and the Mining Institute, UB RAS, Reference No. 124020500031-4.
The authors express their gratitude to A. I. Manevich and A. I. Shakirov for the help in data processing.

keywords Geodynamic zoning, system analysis, discrete mathematical analysis, neural network approach, impermeable strata, Upper Kama Upper Kama Potassium– Magnesium Salt
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