POLYUS, Moscow, Russia

D. A. Selivanov, Head of Structural Geology, selivanovda@polyus.com

Innopolis University, Innopolis, Russia

A. D. Pinigin, Head of AI Technologies Department
A. M. Shagitov, Data Analyst

For structural geological and geomechanical modeling, it is critical to have complete and high-quality initial data. Amongst many types of original data, the key data is the structural and geomechanical core logs. Structural logs record cracks, faults, highly jointed zones, veins, breccia, etc. Geomechanical logging provides various indicators of rock mass quality ratings. Using these data, a geomechanical model of rock mass is built. The authors present a geomechanical and structural core logging procedure using photography and artificial intelligence. The quality of the results and their processing for the creation of a geomechanical data base was evaluated using various methods: comparison of the identified structures with the in-situ geologic logs; comparison of the obtained FF and RQD values with the in-situ geomechanical logs, with the geomechanical logs prepared by an expert, and with photographs, and also with the Priest–Hadson curve. Regarding faulting, its feature is over-identification (with some textural features identified as faults). However, this is connected with the conservative approach to filtering the results, on the basis of criticality of overlooking faults. This method embraces around 10–15 % of the data on faults. The proposed procedure allows obtaining a large bulk of data, at a high quality and within a short term. In prospect, it is planned to implement the procedure as a computer program.

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