Analysis of the time series of seismic and deformation monitoring, obtained from closed works at the Kirovsky mine of JSC "Apatite"

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    The article is devoted to the analysis of the time series, obtained from seismic and deformation monitoring from closed works of Kukisvumchorr deposit JSC "Apatite". The objective of this study is to develop a method for assessing the results of monitoring geomechanical processes in the rock mass on the example of the Kirov mine JSC "Apatit". As a result of closed works, rock masses are changing its natural state of stress. This article has consistently outlined the use of machine learning algorithms in applied problems of geomechanics and geoinformatics. By comparing the schedule of mining operations and seismic activity data with time series of deformations, it is possible to obtain a functional relationship that predicts the distribution of deformations in the rock massif. The results of a computational experiment illustrating the possibility and feasibility of using machine learning algorithms in solving geomechanics problems are presented.



  • Keywords

    Gradient Boosting Algorithm; Time Series; Deformation Monitoring; Khibiny Apatite-Nepheline Deposits.

  • References

      [1] Protosenya A. G. Study of compressive strength of fractured rock mass / A. G. Protosenya, P. E. Verbilo// Notes of the Mining Institute, 2017, Vol. 223— p. 51-57.

      [2] Holt, Charles C. (January–March 2004). "Forecasting Trends and Seasonal by Exponentially Weighted Averages". International Journal of Forecasting. 20 (1): 5–10.

      [3] Brown, Robert Goodell (1963). Smoothing Forecasting and Prediction of Discrete Time Series. Englewood Cliffs, NJ: Prentice-Hall.

      [4] Friedman J.H. Greedy Function Approximation: a Gradient Boosting Machine. Technical Report. Dept. of Statistics, Stanford University, 1999.

      [5] Hastie, T.; Tibshirani, R.; Friedman, J. H. (2009). "10. Boosting and Additive Trees". The Elements of Statistical Learning (2nd ed.). New York: Springer. pp. 337–384.




Article ID: 29888
DOI: 10.14419/ijet.v9i2.29888

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.