ArticleName |
Перспективы нейросетевого моделирования для оценки
опасности затопления рудников Верхнекамского месторождения
калийно-магниевых солей |
ArticleAuthorData |
Геофизический центр РАН, Москва, Россия
Лосев И. В., научный сотрудник, i.losev@gcras.ru
Камаев А. А., инженер лаборатории
Горный институт УрО РАН, Пермь, Россия Евсеев А. В., старший научный сотрудник, канд. техн. наук
ПАО «Уралкалий», Березники, Россия Жукова И. А., начальник отдела |
References |
1. Baryakh А. А., Smirnov E. V., Kvitkin S. Yu., Tenison L.O. Russian potash industry: Issues of rational and safe mining. Gornaya Promyshlennost. 2022. No. 1. pp. 41–50. 2. Baryakh A. A., Samodelkina N. A. Geomechanical estimation of deformation intensity above the flooded potash mine. Journal of Mining Science. 2017. Vol. 53, Iss. 4. pp. 630–642. 3. Baryakh A. A., Samodelkina N. A. Water-tight stratum rupture under large-scale mining. Part II. Journal of Mining Science. 2012. Vol. 48, Iss. 6. pp. 954–961. 4. Baryakh A. A., Gubanova E. A. On flood protection measures for potash mines. Journal of Mining Institute. 2019. Vol. 240. pp. 613–620. 5. Baryakh A. A., Tenison L. O. Justification of engineering safety criteria for undermining of water-proof layer in the Upper Kama Salt Deposit. Gornyi Zhurnal. 2021. No. 4. pp. 57–63. 6. Baryakh A. A., Sanfirov I. A., Fedoseev A. K., Babkin A. I., Tsayukov A. A. Seismic–geomechanical control of water-impervious strata in potassium mines. Journal of Mining Science. 2017. Vol. 53, Iss. 6. pp. 981–992. 7. Baryakh A. A., Tsayukov A. A., Evseev A. V., Lomakin I. S. Mathematical modeling of deformation and failure of salt rock samples. Journal of Mining Science. 2021. Vol. 57, Iss. 3. pp. 370–379. 8. Eremenko V. A., Kosyreva M. A., Vysotin N. G., Khazhy-ylai Ch. V. Geomechanical justification of room-and-pillar dimensions for rock salt and polymineral salt mining. Gornyi Zhurnal. 2021. No. 1. pp. 37–43. 9. Eremenko V. A., Khazhyylai Ch. V., Umarov A. R., Lagutin D. V. Quantitative assessment of rock mass stress–strain behavior at Severomuysky Tunnel. Gornyi Zhurnal. 2023. No. 1. pp. 58–64. 10. Baryakh A. A., Evseev A. V., Glebova P. A., Vasilieva E. L. Ground subsidence prediction from deformation measurements in roadways. Gornyi Zhurnal. 2023. No. 11. pp. 10–14. 11. Eremenko V. A., Vinnikov V. A., Pugach A. S., Kosyreva M. A. Substantiation of rib pillar sizes for rock salt mining in vertical cylindrical stopes arranged at the nodes of regular triangular pattern. Eurasian Mining. 2023. No. 2. pp. 56–62. 12. Phillips J. D., Schwanghart W., Heckmann T. Graph theory in the geosciences. Earth-Science Reviews. 2015. Vol. 143. pp. 147–160. 13. Zhang Y., Li J., Lei Y., Yang M., Cheng P. 3D simulations of salt tectonics in the Kwanza Basin: Insights from analogue and Discrete-Element numerical modeling. Marine and Petroleum Geology. 2020. Vol. 122. ID 104666. 14. Tatarinov V. N., Manevich A. I., Losev I. V. A system approach to geodynamic zoning based on artificial neural networks. Mining Science and Technology. 2018. No. 3. pp. 14–25. 15. Gvishiani A. D., Agayan S. M., Bogoutdinov Sh. R. Investigation of systems of real functions on two-dimensional grids using fuzzy sets. Chebyshevskii Sbornik. 2019. Vol. 20, No. 1(69). pp. 94–111. 16. Gvishiani A. D., Kaftan V. I., Krasnoperov R. I., Tatarinov V. N., Vavilin E. V. Geoinformatics and systems analysis in geophysics and geodynamics. Izvestiya, Physics of the Solid Earth. 2019. Vol. 55, No. 1. pp. 33–49 17. Gvishiani A. D., Agayan S. M., Losev I. V., Tatarinov V. N. Geodynamic hazard assessment of a structural block holding an underground radioactive waste disposal facility. MIAB. 2021. No. 12. pp. 5–18. 18. Agayan S. M., Losev I. V., Belov I. O., Tatarinov V. N., Manevich A. I. et al. Dynamic activity index for feature engineering of geodynamic data for safe underground isolation of high-level radioactive waste. Applied Sciences. 2022. Vol. 12, No. 4. ID 2010. 19. Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B. et al. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research. 2011. Vol. 12. pp. 2825–2830. |