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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 4, issue 5/6
Nat. Hazards Earth Syst. Sci., 4, 641–646, 2004
https://doi.org/10.5194/nhess-4-641-2004
© Author(s) 2004. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

Special issue: Precursory phenomena, seismic hazard evaluation and seismo-tectonic...

Nat. Hazards Earth Syst. Sci., 4, 641–646, 2004
https://doi.org/10.5194/nhess-4-641-2004
© Author(s) 2004. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  18 Oct 2004

18 Oct 2004

A neuro-fuzzy approach to the reliable recognition of electric earthquake precursors

A. Konstantaras1, M. R. Varley1, F. Vallianatos2, G. Collins3, and P. Holifield3 A. Konstantaras et al.
  • 1University of Central Lancashire, ADSIP Research Centre, Department of Technology, Preston PR1 2HE, UK
  • 2Technological Educational Institute of Crete, Department of Natural Resources Engineering, Laboratory of Geophysics and Seismology, Chania, Crete, Greece
  • 3University of Central Lancashire, Faculty of Design and Technology, Preston PR1 2HE, UK

Abstract. 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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