Integration of artificial intelligence into geological and geophysical data processing in solid minerals exploration
EDN: WAUKUO
Abstract
The article provides a systematic review of modern approaches to the use of artificial intelligence methods for processing and interpreting geological and geophysical data in the prospecting and exploration of solid mineral deposits. The key areas of artificial intelligence integration into geological exploration processes are considered: automation of seismic, magnetic, gravimetric and electromagnetic data analysis, recognition of structural and anomalous objects based on machine learning algorithms and deep neural networks, integration of heterogeneous sources of geoinformation using multimodal architectures. The article explores approaches to ore content forecasting, constructing of three-dimensional geological models, and assessing of probabilistic scenarios for ore body location taking into account geological uncertainty. Particular attention is paid to the issues of interpretability of artificial intelligence models, the effect of the quality and completeness of the input data on the reliability of the results obtained, as well as institutional, technical and personnel limitations that hinder the widespread implementation of artificial intelligence in geological exploration practice. The article discusses the development prospects of hybrid intelligent systems that combine expert knowledge and algorithmic methods, as well as the possibility to create digital twins of deposits as a basis for the digital transformation of the mineral resource complex. The article is based on the analysis of relevant publications by Russian and foreign authors and can serve as a methodological guideline for conducting scientific research, creating applied software solutions and increasing the efficiency of digital geology in the context of increasing complexity and cost of geological exploration.
About the Authors
A. K. Krichinsky
Institute of the Earth’s Crust of the Siberian Branch of the Russian Academy of Sciences; Egitinsky Mining and Processing Plant LLC
Russian Federation
Competing Interests:
Russian Federation
Aleksey K. Krichinsky, Postgraduate Student; Geologist
Irkutsk
Competing Interests:
The authors declare no conflicts of interests.
A. V. Pospeev
Institute of the Earth’s Crust of the Siberian Branch of the Russian Academy of Sciences
Russian Federation
Competing Interests:
Russian Federation
Alexander V. Pospeev, Dr. Sci. (Geol. & Mineral.), Professor, Leading Researcher at the Arctic Laboratory
Irkutsk
Competing Interests:
The authors declare no conflicts of interests.
References
1. Qadrouh A.N., Carcione J.M., Alajmi M., Alyousif M.M. A tutorial on machine learning with geophysical applications. Bollettino di Geofisica Teorica ed Applicata. 2019;60(3):375-402. https://doi.org/10.4430/bgta0274.
2. Jooshaki M., Nad A., Michaux S. A systematic review on the application of machine learning in exploiting mineralogical data in mining and mineral industry. Minerals. 2021;11(8):816. https://doi.org/10.3390/min11080816.
3. Jöreskog K.G., Klovan D.I., Reyment R.A. Geological factor analysis; 1976, 178 p. (Russ. ed.: Geologicheskii faktornyi analiz. Leningrad: Nedra; 1980, 223 p.).
4. Rimola A. Mineral exploration technologies: advancements and applications. Journal of Powder Metallurgy & Mining. 2024;13(6):449. Available from: https://www.omicsonline.org/open-access/mineral-exploration-technologies-advancements-and-applications-134337.html [Accessed 17 th May 2025].
5. Corrigan C.C., Ikonnikova S.A. A review of the use of AI in the mining industry: Insights and ethical considerations for multi-objective optimization. The Extractive Industries and Society. 2024;17:101440. https://doi.org/10.1016/j.exis.2024.101440. EDN: EKFQHY.
6. Stepanov I.Yu. The use of machine learning methods in geoinformation models in solving problems of geophysical exploration. Vestnik SSUGT. 2024;29(2):108-117. (In Russ.). https://doi.org/10.33764/2411-1759-2024-29-2-108-117. EDN: BHXCBY.
7. Sun K., Chen Y., Geng G., Lu Z., Zhang W., Song Z., et al. A review of mineral prospectivity mapping using deep learning. Minerals. 2024;14(10):1021. https://doi.org/10.3390/min14101021.
8. Goodfellow I., Bengio Y., Courville A. Deep learning. Cambridge: The MIT Press; 2016, 800 p.
9. Shiverskii G.V., Krivoschekov S.N. Prospects for the application of artificial intelligence methods in the geology of oil and gas. Master’s journal. 2022;2:57-67. (In Russ.). EDN: MXLKEW.
10. Tsaregorodtseva T.K., Gorkin G. M. Application of artificial intelligence in hydrocarbon exploration and production. Nedropol’zovanie XXI vek. 2022;2:12-15. (In Russ.). EDN: FRHXVC.
11. Ostanin V.A. Application prospects of automatic machine learning in the problems of integrated interpretation of geophysical data for the case of Tomsk region zoning by probable oil and gas content. In: Perspektivy razvitiya nauki v sovremennom mire: materialy XII Mezhdunar. nauch.-prakt. konf. = Science development prospects in the modern world: Proceedings of the 12 th International scientific and practical conference. 7 April 2023, Ufa. Ufa: Scientific and Publishing Center Herald of Science LLC; 2023, vol. 2, p. 137-156. (In Russ.). EDN: MGKIUP.
12. LeCun Y., Bengio Y., Hinton G. Deep learning. Nature. 2015;521:436-444. https://doi.org/10.1038/nature14539.
13. Shagarova L.V. Application of artificial intelligence and Big Geodata systems for geological monitoring tasks. In: Novye idei v naukakh o Zemle. Tom 1. Geofizika, geoinformatika, matematicheskoe modelirovanie i iskusstvennyi intellekt v nedropol’zovanii: materialy XVII Mezhdunar. nauch.-prakt. konf. = New Ideas in Earth Sciences. Vol. 1. Geophysics, Geoinformation Sciences, Mathematical Modeling, and Artificial Intelligence in Subsoil Use: Proceedings of the 17 th International scientific and practical conference. 3–4 April 2025, Moscow. Moscow: Russian State University for Geological Prospecting of the Russian Academy of Natural Sciences; 2025, vol. 1, p. 37-40. (In Russ.).
14. Jung H.-S., Lee S. Special issue on machine learning techniques applied to geoscience information system and remote sensing. Applied Sciences. 2019;9(12):2446. https://doi.org/10.3390/app9122446.
15. Panina O.V., Belyaev A.M., Zaval’ko N.A., Eremin S.G., Sagina O.A. Application of deep machine learning methods for structural analysis of ore bodies and prediction of optimal mining zones. Mining industry journal. 2025;1:177-183. (In Russ.). https://doi.org/10.30686/1609-9192-2025-1-177-183. EDN: YAJMMM.
16. Alzubaidi L.S., Zhang J., Humaidi A.J., Al-Dujaili A., Duan Y. Al-Shamma O., et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data. 2021;8(53):1-74. https://doi.org/10.1186/s40537-021-00444-8.
17. Grishkov G.A., Nafigin I.O., Ustinov S.A., Minaev V.A., Petrov V.A. An approach to the creation of spatial predictive prospecting models of deposits based on convolutional neural networks (using the example of the territory of Southeastern Transbaikalia). Science and Technological Developments. 2024;103(2):75-90. (In Russ.). https://doi.org/10.21455/std2024.2-5.
18. Chen L., Wang L., Miao J., Gao H., Zhang Y., Yao Y., et al. Review of the application of Big Data and artificial intelligence in geology. Journal of Physics: Conference Series. 2020;1684(1):012007. https://doi.org/10.1088/1742-6596/1684/1/012007.
19. McGaughey J. Artificial intelligence and Big Data analytics in mining geomechanics. Journal of the Southern African Institute of Mining and Metallurgy. 2020;120(1):15-21. https://doi.org/10.17159/2411-9717/847/2020.
20. Rylnikova M.V., Klebanov D.A., Makeev M.A., Kadochnikov M.V. Application of artificial intelligence and the future of Big Data analytics in the mining industry. Mining industry journal. 2022;3:89-92. (In Russ.). https://doi.org/10.30686/1609-9192-2022-3-89-92. EDN: OEXFGE.
Review
For citations:
Krichinsky A.K., Pospeev A.V. Integration of artificial intelligence into geological and geophysical data processing in solid minerals exploration. Earth sciences and subsoil use. (In Russ.) EDN: WAUKUO
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