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| Titel |
A neuro-fuzzy approach to the reliable recognition of electric earthquake precursors |
| VerfasserIn |
A. Konstantaras, M. R. Varley, F. Vallianatos, G. Collins, P. Holifield |
| 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 ; 4, no. 5/6 ; Nr. 4, no. 5/6 (2004-10-18), S.641-646 |
| Datensatznummer |
250001379
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| Publikation (Nr.) |
 copernicus.org/nhess-4-641-2004.pdf |
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| Zusammenfassung |
| Electric Earthquake Precursor (EEP) recognition is essentially a problem of
weak signal detection. An EEP signal, according to the theory of propagating
cracks, is usually a very weak electric potential anomaly appearing on the
Earth's electric field prior to an earthquake, often unobservable within the
electric background, which is significantly stronger and embedded in noise.
Furthermore, EEP signals vary in terms of duration and size making reliable
recognition even more difficult. An average model for EEP signals has been
identified based on a time function describing the evolution of the number
of propagating cracks. This paper describes the use of neuro-fuzzy networks
(Neural Networks with intrinsic fuzzy logic abilities) for the reliable
recognition of EEP signals within the electric field. Pattern recognition is
performed by the neural network to identify the average EEP model from
within the electric field. Use of the neuro-fuzzy model enables
classification of signals that are not exactly the same, but do approximate
the average EEP model, as EEPs. On the other hand, signals that look like
EEPs but do not approximate enough the average model are suppressed,
preventing false classification. The effectiveness of the proposed network
is demonstrated using electrotelluric data recorded in NW Greece. |
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