The objective of this study is to develop an assessment method for geomechanical monitoring in rocks in terms of the Kirov Mine, Apatit company. Prediction of basic parameters of rock mass stress state, as any other problem arising in data bulk handling, depends both on selection of the number and type of approximating functions. Despite a collection of formal quality metrics and estimation techniques, the approximating models and criteria should be selected individually for each specific array of data. Underground mining alters natural stress state in rocks. This article sets forth algorithms of machine learning in applied problems of geomechanics and geo-information science. Correlation of mine operating schedules and seismic activity data with deformation time-series obtained from deformation monitoring can produce a functional relationship for prediction of strain distribution in rock mass. The article describes the results of the computational experiment which illustrates feasibility and advisability of using machine learning algorithms in solving of geomechanical problems.

For citation:  Gospodarikov A. P., Morozov K. V., Revin I. E. О методе обработки данных сейсмического и деформационного мониторинга при ведении подземных горных работ на примере Кикусвумчоррского месторождения АО «Апатит». MIAB. Mining Inf. Anal. Bull. 2019;(8):157168. [In Russ]. DOI: 10.25018/0236-1493-2019-08-0-157-168.


Algorithm, gradient boosting, monitoring, Khibiny apatite–nepheline deposits, geoinformation science.

Issue number: 8
Year: 2019
ISBN: 0236-1493
UDK: 622.83.551.252
DOI: 10.25018/0236-1493-2019-08-0-157-168
Authors: Gospodarikov A. P., Morozov K. V., Revin I. E.

About authors: A.P. Gospodarikov, Dr. Sci. (Eng.), Professor, K.V. Morozov, Cand. Sci. (Eng.), I.E. Revin1, Graduate Student, e-mail: revine@4inbox.ru, Saint-Petersburg Mining University, 199106, Saint-Petersburg, Russia. Corresponding author: I.E. Revin, e-mail: revine@inbox.ru.


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