Automated geological exploration mapping using convolutional neural structures and data acquisition methods based on swarms of drones

N.L. Krasyukova, K.V. Kharchenko, O.A. Sagina, E.I. Moskvitina, L.K. Babayan
Financial University under the Government of the Russian Federation, Moscow, Russian Federation
Russian Mining Industry №2 / 2025 p.184-191

Abstract: The objective of the study is to develop and validate an integrated approach to improve the accuracy, speed and cost-efficiency of the geological exploration processes. This objective was achieved using the methods of deep learning, swarm intelligence, geoinformation analysis and mathematical modeling. The data collected using a swarm of drones at pilot exploration sites served as the empirical basis of the study. The results obtained show a significant increase in the classification accuracy of geological features (up to 95%), a reduction of the data processing time (by 30-40%) and a decrease in the exploration costs (up to 25%) when using the proposed approach. The practical value of the work consists in creation of a scalable solution for automation and optimization of the exploration mapping processes, which contributes to increasing the efficiency and environmental sustainability of geological projects.

Keywords: geological exploration, convolutional neural networks, methods based on swarms of drones, automated mapping, deep learning, geoinformation analysis

For citation: Krasyukova N.L., Kharchenko K.V., Sagina O.A., Moskvitina E.I., Babayan L.K. Automated geological exploration mapping using convolutional neural structures and data acquisition methods based on swarms of drones. Russian Mining Industry. 2025;(2):184–191. (In Russ.) https://doi.org/10.30686/1609-9192-2025-2-184-191

Article info

Received: 12.01.2025

Revised: 27.02.2025

Accepted: 01.03.2025

Information about the authors

Natalya L. Krasyukova – Dr. Sci. (Econ.), Professor of the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: NLKrasyukova@fa.ru

Konstantin V. Kharchenko – Cand. Sci. (Sociol.), Associate Professor of the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: KVKharchenko@fa.ru

Oksana A. Sagina – Cand. Sci. (Econ.), Associate Professor of the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: oasagina@fa.ru

Ekaterina I. Moskvitina – Cand. Sci. (Econ.), Assistant at the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: EIMoskvitina@fa.ru

Levon K. Babayan – Assistant at the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: LKBabayan@fa.ru

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