Mining College, National University of Science and Technology — MISiS, Moscow, Russia:

Yu. N. Kuznetsov, Professor, Doctor of Engineering Sciences
D. A. Stadnik, Associate Professor, Candidate of Engineering Sciences, denstadnik@rambler.ru
N. M. Stadnik, Post-Graduate Student

Micromine Company, Moscow, Russia:
B. V. Kurtsev, Chief Executive Officer

Search and selection of an advanced engineering solution in mineral mining is based on identification of uniform geological structures (geostructures). To make a geostructure identification unbiased and for the appropriate decision-making, an expert studies immense number of geological characteristics presented as 3D models. However, the modern programs on mining and geological engineering lack tools for automated identification of geostructures. The authors suggest using 3D models of Micromine’s software. A deposit is divided into geometrized parts (blocks) with nominally uniform properties of rocks, and data on these blocks are then processed. It is feasible to use artificial neural networks for the analysis. Having reviewed the ANN methods, the authors recommend Kohonen’s self-organizing network for automated clustering of coal fields. As a result, the authors have formulated an integrated model for identification of geostructures in 3D block model of coal fields for automated substantiation of rational project solutions. The model for identification of geostructures in a coal field has three constituents: input geological data; 3D model of the coal field based on the data from mining and geological information system; selforganizing neural network for data clustering. The operation of identification of geostructures was tested. As a test subject, a coal bed site 800×800 m in area was chosen. The test trial discovered two geostructures. Mining in geostructure 1 is expedient by up-dip longwalling, considering high water content and average thickness of the coal bed, and mining in geostructure 2 is better to implement along the strike. Verification of the efficiency of the procedure proposed for clustering coal bed reserves allows a conclusion on feasibility of the objective automated identification of geostructures to make mining easy and safe. Thereunder, the model of identification of geostructures was constructed for a coal bed in Severnaya Mine, SUEK-Kuzbass. The model showed a single data cluster, which is an evidence of a single geological structure accommodating all coal reserves of the bed, and this was confirmed in actual mining. Thus, the introduction of integrated identification of geostructures in 3D model of coal fields allows delineating mine areas with uniform parameters, suitable for mining with monotechnology.
The authors appreciate participation of S. S. Volkov, Postgraduate student, Mining College, NUST MISiS in this study.
The authors express their gratitude to MICROMINE for the geology and mine planning software, procedural support.

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