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| Titel |
An unsupervised learning algorithm: application to the discrimination of seismic events and quarry blasts in the vicinity of Istanbul |
| VerfasserIn |
H. S. Kuyuk, E. Yildirim, E. Dogan, G. Horasan |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 11, no. 1 ; Nr. 11, no. 1 (2011-01-08), S.93-100 |
| Datensatznummer |
250009039
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| Publikation (Nr.) |
 copernicus.org/nhess-11-93-2011.pdf |
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| Zusammenfassung |
| 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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