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ArticleName Integrated approach to prediction of dynamic phenomena in difficult geological conditions in coal mines using geophysical and satellite survey data
DOI 10.17580/gzh.2024.01.10
ArticleAuthor Grechishkin P. V., Giniyatullina O. L., Troshkov N. Yu., Panin S. F.

VNIMI’s Division in Kemerovo, Kemerovo, Russia

P. V. Grechishkin, Director, Candidate of Engineering Sciences,
O. L. Giniyatullina, Researcher, Candidate of Engineering Sciences
N. Yu. Troshkov, Deputy Director
S. F. Panin, Head of Laboratory


Top-downward cutting of thick coal seams faces the problem connected with prediction of dynamic phenomena. There are some aspects associated with forming-up of a system of emergency prevention, starting from acquisition of data on the stress–strain behavior of rock mass and finishing with the data interpretation and the required preventive activities. This study addresses the issue of local monitoring of dynamic phenomena during topdownward cutting of thick coal seams. It is proposed to predict dynamic phenomena by integrating the regional and local prediction methods using algorithms of searching potentially hazardous zones as a result of processing of the remote sensing data and artificial intelligence scenarios. The technology is implemented in the form of indicator panels which display the level of hazard per a preset index. The algorithms are presented for processing data on seismic events with identification of potentially hazardous zones in mines. It is demonstrated that the theoretical seismic data correlate with the vertical displacements from remote sensing of ground surface within the mine field. After learning of neural network, it is possible to predict the maximal energy of seismic events for 12 h within a mine field using the catalog of seismic events at a confidence coefficient higher than 90 %. While the seismic and satellite data are being collected and correlated with the predictions of dynamic events using local methods, it is expected to obtain persistent signs and to automate prediction of the dynamic phenomena induced by rock pressures in coal mines.

keywords Forecast of dynamic phenomena, indicator panel, artificial neural network, seismic energy release centers, radar interferometry, vertical displacements of ground surface

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