Geoinformation forecast and exploration model of the prospective Tuyukan uranium ore site

DOI: https://doi.org/10.30686/1609-9192-2025-6-138-145

Читать на русском языке S.A. Ustinov1, V.A. Petrov1, N.A. Grebenkin2, I.A. Kochkin1, A.M. Chepchugov1, 2
1 Institute of Geology of Ore Deposits, Petrography, Mineralogy and Geochemistry of the Russian Academy of Sciences, Moscow, Russian Federation
2 All-Russian Research Institute of Mineral Raw Materials named after. N.M. Fedorovsky, Moscow, Russian Federation
Russian Mining Industry №6/ 2025 p. 138-145

Abstract: A local forecast and exploration model has been created based on application of modern geoinformation technologies for the promising Tuyukan uranium ore site located in the Mamsko-Chuisky district of the Irkutsk region. This model is the final result of the authors' work on forecast and metallogenic multi-scale mapping of new, mainly uranium, ore objects within the considered area. Processing of heterogeneous Earth remote sensing data in combination with the author's methods of structural-geomorphological and tectonophysical analysis was carried out using GIS technologies at the previous stages of the study, for the part of O-49-XII state geological map at the scale of 1:200 000 that includes the considered site. Criteria for localization of uranium mineralization were identified based on the results, and a regional weight-based forecast and exploration model of the territory was created. Analysis of this model made it possible to clearly identify the Tuyukan site as a promising asset. Based on the regional forecast and exploration model and additional information obtained as the results of various surveys during the geological exploration stage, additional forecast criteria were formulated and visualized in the GIS for the promising Tuyukan site, which made it possible to create a local weight-based forecast and exploration model mainly focused on searching for uranium deposits. As the result, promising areas were localized within the site and recommendations for further exploration work were formulated.

Keywords: geoinformation technologies, forecast and exploration model, metallogeny, uranium, structures of ore fields and deposits, Patom Highlands, Tonodskoye Upland, Tuyukan deposit

Acknowledgements: The research was supported by the Russian Science Foundation Grant No. 24-27-00218, https://rscf.ru/project/24-27-00218/.

For citation: Ustinov S.A., Petrov V.A., Grebenkin N.A., Kochkin I.A., Chepchugov A.M. Geoinformation forecast and exploration model of the prospective Tuyukan uranium ore site. Russian Mining Industry. 2025;(6):138–145. (In Russ.) https://doi.org/10.30686/1609-9192-2025-6-138-145


Article info

Received: 02.09.2025

Revised: 27.10.2025

Accepted: 17.11.2025


Information about the authors

Stepan A. Ustinov – Cand. Sci. (Geol. & Mineral.), Deputy Director for Research, Leading Researcher, Institute of Ore Deposit Geology, Petrography, Mineralogy and Geochemistry of the Russian Academy of Sciences, Moscow, Russian Federation; https://orcid.org/0000-0002-6679-9607; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Vladislav A. Petrov – Corresponding Member of RAS, Dr. Sci. (Geol. & Mineral.) Director, Institute of Ore Deposit Geology, Petrography, Mineralogy and Geochemistry of the Russian Academy of Sciences, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Nikolay A. Grebenkin – Cand. Sci. (Geol. & Mineral.), Head of the Department of Uranium and Rare Metals, All-Russian Research Institute of Mineral Resources named after N.M. Fedorovsky, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Igor A. Kochkin – Postgraduate Student, Junior Researcher, Institute of Ore Deposit Geology, Petrography, Mineralogy and Geochemistry of the Russian Academy of Sciences, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Artur M. Chepchugov – Postgraduate Student, Laboratory Assistant-Researcher, Institute of Ore Deposit Geology, Petrography, Mineralogy and Geochemistry of the Russian Academy of Sciences; Engineer, All-Russian Scientific Research Institute of Mineral Resources named after N.M. Fedorovsky, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.


References

1. Makarev L.B., Mironov Yu.B. Features of metallogeny and prospects for industrial uranium content of the Chuya-Tonod mineragenic zone of northern Transbaikalia (based on the materials of GK-1000/3 and GDP-200/2). Regional Geology and Metallogeny. 2014;(57):87–94. (In Russ.) Available at: https://karpinskyinstitute.ru/ru/public/reggeology_met/content/2014/57/57_10.pdf (accessed: 15.05.2025).

2. Makarev L.B., Efremova U.S., Krymsky R.Sh., Sergeev S.A. Age and stages of uranium mineralization in the tuyukan ore cluster (Tonod district, Northern Transbaikalia). Regional Geology and Metallogeny. 2019;(77):67–74. (In Russ.) Available at: https://karpinskyinstitute.ru/ru/public/reggeology_met/content/2019/77/77_08.pdf (accessed: 15.05.2025).

3. Mashkovtsev G.A., Konstantinov A.K., Miguta A.K., Shumilin M.V., Shchetochkin V.N. Uran rossiyskikh nedr. M.: VIMS; 2010. 850 s. Available at: https://www.geokniga.org/books/10630 (accessed: 15.05.2025).

4. Mitrofanova N.N., Boldyrev V.I., Korobeinikov N.K., Mitrofanov G.L., Knutova S.V., Semeykina L.K., et al. Gosudarstvennaya geologicheskaya karta Rossiyskoy Federatsii. Masshtab 1 : 1 000 000 (tret’e pokolenie). Seriya Aldano-Zabaykal’skaya. List O-49 – Kirensk. Ob”yasnitel’naya zapiska. SPb.: Kartfabrika VSEGEI; 2012. 607 s.

5. Ustinov S.A., Petrov V.A., Minaev V.A., Chepchugov A.M., Svecherevskiy A.D., Kochkin I.A. Forecast and prospecting model of the Tuyukan uranium ore cluster area based on the earth remote sensing data and structural-tectonophysical approach. Prospect and protection of Mineral Resources. 2024;(4):52–65. (In Russ.) https://doi.org/10.53085/0034-026X_2024_4_52

6. Sizykh V.I. Sharyazhno-nadvigovaya tektonika okrain drevnikh platform. Novosibirsk: Izd-vo SO RAN; GEO; 2001. 152 s. Available at: https://www.geokniga.org/books/29690 (accessed: 15.05.2025).

7. Gitis V.G., Shchukin Yu.K., Starostin V.I. GIS technology for ore deposit forecasting. Information Processes. 2013;13(2):48–63. (In Russ.) Available at: http://www.jip.ru/2013/48-62-2013.pdf (accessed: 15.05.2025).

8. Bonham-Carter G.F., Agterberg F.P., Wright D.F. Weights of evidence modelling: a new approach to mapping mineral potential. In: Agterberg F.P., Bonham-Carter G.F. (eds.) Statistical applications in the earth sciences. Geological Survey of Canada; 1990, pp. 171–183. https://doi.org/10.4095/128059

9. Xiao K., Xiang J., Fan M., Xu Y. 3D mineral prospectivity mapping based on deep metallogenic prediction theory: A case study of the Lala Copper Mine, Sichuan, China. Journal of Earth Science. 2021;32(2):348–357. https://doi.org/10.1007/s12583-21-1437-8

10. Baddeley A., Brown W., Milne R.K., Nair G., Rakshit S., Lawrence T. et al. Optimal thresholding of predictors in mineral prospectivity analysis. Natural Resources Research. 2021;30:923–969. https://doi.org/10.1007/s11053-020-09769-2

11. Liu Y., Carranza E.J.M., Xia Q. Developments in quantitative assessment and modeling of mineral resource potential: An overview. Natural Resources Research. 2022;31(4):1825–1840. https://doi.org/10.1007/s11053-022-10075-2

12. Xi W., Ping Y.Y., Tao J.T., Liu C., Shen J., Zhang Y.W. Predicting copper-polymetallic deposits in Kalatag using the weight of evidence model and novel data sources. Open Geosciences. 2023;15(1):20220588. https://doi.org/10.1515/geo-2022-0588

13. Ustinov S.A., Chepchugov A.M., Tomarovskaya M.A., Petrov V.A., Svecherevskiy A.D., Yarovaya E.V. Structural–tectonophysical approach to interpreting lineament analysis results for the prediction of ore-forming mineral systems using the example of the Tuyukan ore cluster area. Izvestiya, Atmospheric and Oceanic Physics. 2024. Т. 60. № 12. С. 1547-1565. DOI: 10.1134/S0001433825700215. https://doi.org/10.1134/S0001433825700215

14. Grishkov G.A., Nafigin I.O., Ustinov S.A., Petrov V.A., Minaev V.A. Developing a technique for automatic lineament identification based on the neural network approach. Izvestiya, Atmospheric and Oceanic Physics. 2023;59(10):1271–1280. https://doi.org/10.1134/s0001433823120101