Norilsk Nickel’s Polar Division, Norilsk, Russia:

M. P. Sergunin, Head of Department for Engineering Supervision of Mining, SerguninMP@nornik.ru
D. P. Seleskerov, Deputy Director of Taimyrsky Mine – Chief Engineer

Gipronickel Institute, Saint-Petersburg, Russia:
A. V. Trofimov, Head of Geotechnique Laboratory, C andidate of Engineering Sciences

Fedorovsky Polar State University, Norilsk, Russia:
O. S. Danilov, Associate Professor at Mineral Mining Department, Candidate of Engineering Sciences

The modern stage of mineral mining involves prediction of geotechnical risks. One of the major methods in this regard is the geodynamic zoning. The key target is to assess the cross-effect of mining operations and blocks structure of the Earth’s crust. This article presents the information on ground surface subsidence within the boundary of the mining lease of Taimyrsky Mine as a result of processing of paired images from Sentinel-1A sate llite. The implemented probabilistic kinematic analysis revealed some features of movements in overlying rock mass, in particular, local uplifts of ground surface. The detailed information on vertical deformations was obtained. Such approach enabled detecting the deformation areas subjected to the effect of the global tectonics which is, as a rule, neglected in geotechnical risk assessment while it directly affects mining safety. Despite the apparently consistent patterns resulted from processing of satellite images, the method InSAR is only used as a supplemental technique since the relevant regulatory framework is yet absent. In combination with the conventional surveying for monitoring ground surface deformations, the method InSAR enhances efficiency of monitoring and improves accuracy of geodynamic risk prediction in the course of mineral mining, inclusive of observations over the global tectonic processes. The probabilistic kinematic analysis allows for getting insight into the mechanism of overlying rock mass movement with regard to rock mass jointing and, jointly with InSAR, can be used in the artificial intelligence systems for learning models of prediction of overlying rock movement parameters during mineral mining.

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