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| Titel |
Thermal anomalies detection before strong earthquakes (M > 6.0) using interquartile, wavelet and Kalman filter methods |
| VerfasserIn |
M. R. Saradjian, M. Akhoondzadeh |
| 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. 4 ; Nr. 11, no. 4 (2011-04-12), S.1099-1108 |
| Datensatznummer |
250009342
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| Publikation (Nr.) |
 copernicus.org/nhess-11-1099-2011.pdf |
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| Zusammenfassung |
| Thermal anomaly is known as a significant precursor of strong earthquakes,
therefore Land Surface Temperature (LST) time series have been analyzed in
this study to locate relevant anomalous variations prior to the Bam (26 December
2003), Zarand (22 February 2005) and Borujerd (31 March 2006) earthquakes. The
duration of the three datasets which are comprised of MODIS LST images is
44, 28 and 46 days for the Bam, Zarand and Borujerd earthquakes, respectively.
In order to exclude variations of LST from temperature seasonal effects, Air
Temperature (AT) data derived from the meteorological stations close to the
earthquakes epicenters have been taken into account. The detection of
thermal anomalies has been assessed using interquartile, wavelet transform
and Kalman filter methods, each presenting its own independent property in
anomaly detection. The interquartile method has been used to construct
the higher and lower bounds in LST data to detect disturbed states outside
the bounds which might be associated with impending earthquakes. The wavelet
transform method has been used to locate local maxima within each time
series of LST data for identifying earthquake anomalies by a predefined
threshold. Also, the prediction property of the Kalman filter has been used in
the detection process of prominent LST anomalies. The results concerning the
methodology indicate that the interquartile method is capable of detecting
the highest intensity anomaly values, the wavelet transform is sensitive to
sudden changes, and the Kalman filter method significantly detects the highest
unpredictable variations of LST. The three methods detected anomalous
occurrences during 1 to 20 days prior to the earthquakes showing close
agreement in results found between the different applied methods on LST data
in the detection of pre-seismic anomalies. The proposed method for anomaly
detection was also applied on regions irrelevant to earthquakes for which no
anomaly was detected, indicating that the anomalous behaviors can be related
to impending earthquakes. The proposed method receives its credibility from the
overall capabilities of the three integrated methods. |
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