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| Titel |
Hidden semi-Markov Model based earthquake classification system using Weighted Finite-State Transducers |
| VerfasserIn |
M. Beyreuther, J. Wassermann |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 18, no. 1 ; Nr. 18, no. 1 (2011-02-14), S.81-89 |
| Datensatznummer |
250013869
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| Publikation (Nr.) |
 copernicus.org/npg-18-81-2011.pdf |
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| Zusammenfassung |
| Automatic earthquake detection and classification is required
for efficient analysis of large seismic datasets. Such techniques
are particularly important now because
access to measures of ground motion is nearly unlimited
and
the target waveforms (earthquakes) are often hard to
detect and classify.
Here, we propose to use models from speech synthesis
which extend the double stochastic models from speech recognition by
integrating a more realistic duration of the target waveforms.
The method, which has general applicability, is applied to
earthquake detection and classification.
First, we generate characteristic functions from the time-series. The
Hidden semi-Markov Models are estimated from the characteristic functions
and Weighted Finite-State Transducers are
constructed for the classification.
We test our scheme on one month of continuous seismic data, which
corresponds to 370 151
classifications, showing that incorporating the time dependency
explicitly in the
models significantly improves the results compared to Hidden Markov
Models. |
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