Investigation of the characteristics of geoelectric field signals prior to earthquakes using adaptive STFT techniques
An earthquake is one of the most destructive natural disasters that can occur, often killing many people and causing large material losses. Hence, the ability to predict earthquakes may reduce the catastrophic effects caused by this phenomenon. The geoelectric field is a feature that can be used to...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Copernicus GmbH (Copernicus Publications)
2013
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/30091/1/nhess-2012-460-manuscript-version2.pdf http://irep.iium.edu.my/30091/5/p.nhess-13-1679-2013.published.pdf http://irep.iium.edu.my/30091/ http://www.nat-hazards-earth-syst-sci.net/13/1679/2013/nhess-13-1679-2013.html |
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| Summary: | An earthquake is one of the most destructive natural disasters that can occur, often killing many people and causing large material losses. Hence, the ability to predict earthquakes may reduce the catastrophic effects caused by this phenomenon. The geoelectric field is a feature that can be used to predict earthquakes (EQs) because of significant changes in the amplitude of the signal prior to an earthquake. This paper presents a detailed analysis of geoelectric field signals of earthquakes which occurred in 2008 in Greece. In 2008, 12 earthquakes occurred in Greece. Five of them were recorded with magnitudes greater than Ms = 5R (5R), while seven of them were recorded with magnitudes greater than Ms = 6R (6R). In the analysis, the 1st significant changes of the geoelectric field signal are detected. Then, the signal is segmented and windowed. The adaptive short-time Fourier transform (adaptive STFT) technique is then applied to the windowed signal, and the spectral analysis is performed thereafter. The results show that the 1st significant changes of the geoelectric field prior to an earthquake have a significant amplitude frequency spectrum compared to other conditions, i.e. normal days and the day of the earthquake, which can be used as input parameters for earthquake prediction.
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