Development of mining and geological information system in the present-day situation in the Russian mining industry

DOI: https://doi.org/10.30686/1609-9192-2023-5S-35-40

Читать на русскоя языкеO.V. Nagovitsyn
Mining Institute Kola Science Centre of the Russian Academy of Sciences, Apatity, Russian Federation
Russian Mining Industry №5S / 2023 р. 35-40

Abstract: The article presents an approach to the development of the MINEFRAME mining and geological information system. The current situation in the mining industry, the foreign policy challenges require to use digital technologies as a driver to improve labor productivity and industrial safety. One of the most important classes of software used at mining enterprises are mining and geological information systems, i.e. the information systems that offer complex automation in solving geological, mine surveying and technological tasks when working with the vector, wireframe and block models of mining system facilities. The mining and geological information systems make it possible to have reliable knowledge of the deposit at each stage of exploration, design and operation of the mine through the use of 3D geological and geomechanical models, determination of distribution patterns for useful and harmful components of minerals for comprehensive analysis of scenarios of the sequence of field development, selection of the most efficient mining technologies and equipment, assessment of the risk level for design solutions and their economic efficiency. This approach fully corresponds to the trends of digital transformation declared by the government of the Russian Federation and represents a new stage of automation and informatization of economic activities and transition to digital technologies.

Keywords: digitalization, optimization, geostatistics, mining and geological information systems, mining, geology, mine surveying

For citation: Nagovitsyn O.V. Development of mining and geological information system in the present-day situation in the Russian mining industry. Russian Mining Industry. 2023;(6S):35–40. https://doi.org/10.30686/1609-9192-2023-6S-35-40

Article info

Received: 09.10.2023

Revised: 22.11.2023

Accepted: 28.11.2023

Information about the author

Oleg V. Nagovitsyn – Dr. Sci. (Eng.), Deputy Director for Research, Head of Laboratory of Integrated Subsoil Development and Conservation Theory, Mining Institute of the Kola Scientific Center of the Russian Academy of Sciences, Apatity, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

References

1. Barnewold L. Digital technology trends and their implementation in the mining industry. In: Mueller C., Assibey-Bonsu W., Baafi E., Dauber C., Doran C., Jaszczuk M., Nagovitsyn O. (eds) Mining goes Digital: Proceedings of the 39th International Symposium “Application of Computers and Operations Research in the Mineral Industry” (APCOM 2019), June 4–6, 2019, Wroclaw, Poland. CRC Press; 2019, pp. 9–16. https://doi.org/10.1201/9780429320774-2

2. Lukichev S.V. Development of the Russian software is not only a solution to the import substitution challenge, but also a way to ensure the country's technological independence. Russian Mining Industry. 2022;(2):12–14. (In Russ.)

3. Kaputin Yu.E. Information technologies and economic assessment of mining projects (for mining engineers). St. Petersburg: Nedra; 2008. 396 p. (In Russ.) Available at: https://www.geokniga.org/books/7023

4. Lukichev S.V. (ed.) Scientific and practical aspects of applying digital technologies in the mining industry. Apatity: Kola Science Centre of the Russian Academy of Sciences; 2019. 192 p. (In Russ.)

5. Lukichev S.V., Nagovitsin O.V. Digital transformation of mining industry: Past, Present and Future. Gornyi Zhurnal. 2020;(9):13–18. (In Russ.) https://doi.org/10.17580/gzh.2020.09.01

6. Lukichev S.V., Nagovitsyn O.V., Ilin E.A., Rudin R.S. Digital technologies for sustainable engineering in mining – The first step towards a “smart mine”. Gornyi Zhurnal. 2018;(7):86–90. (In Russ.) https://doi.org/10.17580/gzh.2018.07.17

7. Kosnikov Yu.N. Radial basis functions use in geometric modeling of three-dimensional objects of visualization. Models, Systems, Networks in Economics, Technology, Nature and Society. 2020;(4):55–70. (In Russ.) https://doi.org/10.21685/2227-8486-2020-4-6

8. Huang L., Balamurali M., Silversides K. L. Machine learning classification of geochemical and geophysical data. In: Mueller C., AssibeyBonsu W., Baafi E., Dauber C., Doran C., Jaszczuk M., Nagovitsyn O. (eds) Mining goes Digital: Proceedings of the 39th International Symposium “Application of Computers and Operations Research in the Mineral Industry” (APCOM 2019), June 4–6, 2019, Wroclaw, Poland. CRC Press; 2019, pp. 101–105. https://doi.org/10.1201/9780429320774-12

9. Avalos S., Ortiz J.M. Recursive convolutional neural networks in a multiple-point statistics framework. In: Mueller C., Assibey-Bonsu W., Baafi E., Dauber C., Doran C., Jaszczuk M., Nagovitsyn O. (eds) Mining goes Digital: Proceedings of the 39th International Symposium “Application of Computers and Operations Research in the Mineral Industry” (APCOM 2019), June 4–6, 2019, Wroclaw, Poland. CRC Press; 2019, pp. 168–176. https://doi.org/10.1201/9780429320774-19