Detailed information



A grand problem in continuous geomonitoring is constituted by a huge amount of accumulated data to be analyzed and interpreted. In this regard, it is relevant to carry out the preliminary express-analyses of data in order to select representative points to be in spotlight in the first place in the studied rock mass. By way of approach to this problem solution, the authors propose the clustering analysis which produces clustering centers usable as representative points. The scope of the discussion embraces three clustering algorithms: k-means, Mean Shift and DBSCAN. Their efficiency and suitability to the seismic data analysis is assessed by comparing the results of the algorithms with the representative points found by expert for the pre-assigned set of data. The quality of the clustering procedure is evaluated by the Calinski–Harabasz and Davies–Bouldin indexes, as well as the silhouette coefficient. The set of the seismic data was composed of numerical values of stress state (rock pressure) and fluid flow potential in rock mass obtained in local prediction. The obtained results allow concluding that the best clustering algorithm is DBSCAN, and it is applicable to preliminary express-analysis of seismic data.

For citation: Abdrakhmanov M. I., Lapin S. E., Shnayder I. V. Clustering algorithms in express-analysis of seismic data. MIAB. Mining Inf. Anal. Bull. 2019;(6):27-44. [In Russ]. DOI: 10.25018/0236-1493-2019-06-0-27-44.

: 6
ISBN: 0236-1493
: 622.817.4
DOI: 10.25018/0236-1493-2019-06-0-27-44
Authors: Abdrakhmanov M. I., Lapin S. E., Shnayder I. V.

Authors' Information:
M.I. Abdrakhmanov, Cand. Sci. (Eng.), Chief Specialist, e-mail: marat-ab@mail.ru,
LLC Information mining technologies, Ekaterinburg, Russia,
S.E. Lapin (1), Cand. Sci. (Eng.), Senior Researcher, e-mail: sergei.l@bk.ru,
I.V. Shnayder (1), Graduate Student,
1) Ural State Mining University, 620144, Ekaterinburg, Russia.
Corresponding author: M.I. Abdrakhmanov, e-mail: marat-ab@mail.ru.

Key words:
Geomonitoring, seismic data analysis, clustering analysis, express-analysis, clustering metrics.


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