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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 19, issue 4
Nonlin. Processes Geophys., 19, 411–419, 2012
https://doi.org/10.5194/npg-19-411-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 19, 411–419, 2012
https://doi.org/10.5194/npg-19-411-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 03 Aug 2012

Research article | 03 Aug 2012

Application of k-means and Gaussian mixture model for classification of seismic activities in Istanbul

H. S. Kuyuk1,2, E. Yildirim3, E. Dogan1, and G. Horasan3 H. S. Kuyuk et al.
  • 1Department of Civil Engineering, Sakarya University, Turkey
  • 2Seismological Laboratory, University of California, Berkeley, USA
  • 3Department of Geophysical Engineering, Sakarya University, Turkey

Abstract. Two unsupervised pattern recognition algorithms, k-means, and Gaussian mixture model (GMM) analyses have been applied to classify seismic events in the vicinity of Istanbul. Earthquakes, which are occurring at different seismicity rates and extensions of the Thrace-Eskisehir Fault Zone and the North Anatolian Fault (NAF), Turkey, are being contaminated by quarries operated around Istanbul. We have used two time variant parameters, complexity, the ratio of integrated powers of the velocity seismogram, and S/P amplitude ratio as classifiers by using waveforms of 179 events (1.8 < M < 3.0). We have compared two algorithms with classical multivariate linear/quadratic discriminant analyses. The total accuracies of the models for GMM, k-means, linear discriminant function (LDF), and quadratic discriminant function (QDF) are 96.1%, 95.0%, 96.1%, 96.6%, respectively. The performances of models are discussed for earthquakes and quarry blasts separately. All methods clustered the seismic events acceptably where QDF slightly gave better improvements compared to others. We have found that unsupervised clustering algorithms, for which no a-prior target information is available, display a similar discriminatory power as supervised methods of discriminant analysis.

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