Application of artificial intelligence algorithm in processing structural geological models

An example of learning a neural network capable of processing and classifying triangulation frameworks of structural geological models is presented. The classification of the frameworks uses the proper calculated vectors of lithological surfaces. The accuracy of the models is 98.2%, which confirms the usability of the machine learning algorithm in classifying and selecting objects at the increased speed of data processing and at the reduced likelihood of errors due to the human factor. After separating the frameworks of tectonic faults and lithological contacts from structural geological models, an additional information on spatial position (average dip angle, dip azimuth) and surface characteristics, such as roughness and waviness, is determined for each framework, which allows using the obtained data to perform kinematic analyses and for solving problems connected with rock movements. The Woodcock coefficients and the eigenvalues of the vectors are used as input data in the model being trained. These characteristics can be used for a refined assessment of the state of a block massif and used to determine the degree of adhesion of tectonic faults, which makes it possible to improve the accuracy of predictive models and subsequently be used to train models that describe the parameters of displacement of the overlying rock mass.

Keywords: faulting, geological model, kinematic analysis, neural network, specification of fabric shapes, eigenvector methods, Woodcock's fabric parameters, rock movements.
For citation:

Sergunin M. P., Eremenko V. A. Application of artificial intelligence algorithm in processing structural geological models. MIAB. Mining Inf. Anal. Bull. 2023;(9):56-67. [In Russ]. DOI: 10.25018/0236_1493_2023_9_0_56.

Issue number: 9
Year: 2023
Page number: 56-67
ISBN: 0236-1493
UDK: 622.2; 622.831
DOI: 10.25018/0236_1493_2023_9_0_56
Article receipt date: 06.06.2023
Date of review receipt: 26.06.2023
Date of the editorial board′s decision on the article′s publishing: 10.08.2023
About authors:

M.P. Sergunin, Head of Department, Center for Geodynamic Safety, Polar Division of MMC Norilsk Nickel, Norilsk, Russia, e-mail:, ORCID ID: 0000-0002-7774-6826,
V.A. Eremenko, Dr. Sci. (Eng.), Professor of Russian Academy of Sciences, Director of the Research Center for Applied Geomechanics and Convergent Technologies in Mining, Professor at Department of Physical Processes in Mining and Geocontrol, Mining Institute, National University of Science and Technology «MISiS», 119049, Moscow, Russia, e-mail:, ORCID ID: 0000-0003-1478-6916.

For contacts:

M.P. Sergunin, e-mail:


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