NUST MISIS’ College of Mining, Moscow, Russia

A. S. Fedyanin, R&D Engineer, PhD, Associate Professor, uz_platinum@mail.ru
V. A. Eremenko, Director of Research Center for Applied Geomechanics and Convergent Geotechnologies, Doctor of Engineering Sciences, Professor of the Russian Academy of Sciences

This article addresses the pressing challenge of forecasting geomechanical accidents in underground mines. A key issue is the integrated analysis of heterogeneous data (geodetic measurements, expert assessments, statistical data) obtained from existing monitoring systems. As a solution, a method is proposed for the data processing unification based on converting the data into time series followed by the calculation of a universal risk indicator– the probability of a critical value of a control parameter within a specified time interval. The novelty of the work lies in the development of an integrated probabilistic–statistical approach that enables the comparison and aggregation of risks from diverse processes within a single risk-oriented model. The article presents mathematical forecasting models for different data types, a methodology for transitioning to probabilistic assessment, and demonstration calculations using real-world examples. It is shown that this approach establishes a foundation for building comprehensive decision support systems in the mining industry. The advantages of the approach include: its universality—the approach is applicable with data of any physical nature recorded in time; compatibility—comparison of risks of different processes; proactivity—the approach ensures a qualitative basis for the proactive decision-making. Regarding the main limitations and introduction issues, the quality of a forecast drastically depends on the correctness of a selected model and on the data representativeness; the critical values are determined in specific geological conditions and can be subjective. The promising trends of the further research are: more complex models of time series; machine learning for automated detection of trends and anomalies; development of methods for aggregating partial probabilities Pfail into an integrated risk criterion of a whole object.

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