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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 11, issue 4
Nat. Hazards Earth Syst. Sci., 11, 1099–1108, 2011
https://doi.org/10.5194/nhess-11-1099-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Progress in research on earthquake precursors

Nat. Hazards Earth Syst. Sci., 11, 1099–1108, 2011
https://doi.org/10.5194/nhess-11-1099-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 12 Apr 2011

Research article | 12 Apr 2011

Thermal anomalies detection before strong earthquakes (M > 6.0) using interquartile, wavelet and Kalman filter methods

M. R. Saradjian and M. Akhoondzadeh M. R. Saradjian and M. Akhoondzadeh
  • Remote Sensing Division, Surveying and Geomatics Engineering Department, University College of Engineering, University of Tehran, Tehran, Iran

Abstract. 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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