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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 1 | Copyright
Nat. Hazards Earth Syst. Sci., 11, 93-100, 2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 08 Jan 2011

Research article | 08 Jan 2011

An unsupervised learning algorithm: application to the discrimination of seismic events and quarry blasts in the vicinity of Istanbul

H. S. Kuyuk1, E. Yildirim2, E. Dogan1, and G. Horasan2 H. S. Kuyuk et al.
  • 1Department of Civil Engineering, Sakarya University, Sakarya, Turkey
  • 2Department of Geophysical Engineering, Sakarya University, Sakarya, Turkey

Abstract. The results of the application of an unsupervised learning (neural network) approach comprising a Self Organizing Map (SOM), to distinguish micro-earthquakes from quarry blasts in the vicinity of Istanbul, Turkey, are presented and discussed. The SOM is constructed as a neural classifier and complementary reliability estimator to distinguish seismic events, and was employed for varying map sizes. Input parameters consisting of frequency and time domain data (complexity, spectral ratio, S/P wave amplitude peak ratio and origin time of events) extracted from the vertical components of digital seismograms were estimated as discriminants for 179 (1.8 < Md < 3.0) local events. The results show that complexity and amplitude peak ratio parameters of the observed velocity seismogram may suffice for a reliable discrimination, while origin time and spectral ratio were found to be fuzzy and misleading classifiers for this problem. The SOM discussed here achieved a discrimination reliability that could be employed routinely in observatory practice; however, about 6% of all events were classified as ambiguous cases. This approach was developed independently for this particular classification, but it could be applied to different earthquake regions.

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