Thermal anomalies detection before earthquake using three filters (Fourier, Wavelet and Logarithmic Differential Filter), A Case Study of two Earthquakes in Iran

Earthquake is one of the most destructive natural phenomena which has human and financial losses. The existence of an efficient prediction system and early warning system will be useful for reducing effects of destroying earthquake. In this research, the soil temperature time-series data, obtained from three meteorological station, using three filters (Fourier, Wavelet and Logarithmic Differential Filter (LDF)), have been analyzed in order to characterize the thermal anomalies behavior before the major earthquakes. These methods, were performed on the Bam earthquake (2003), and the Zarand earthquake (2005) in Iran. The results indicate thermal anomalies appeared before earthquakes and are in relation to the major earthquake. In addition, the results revealed that the LDF can clearly detect thermal anomaly in the all cases while the other methods (Fourier, Wavelet) could detect temperature changes before some earthquakes. Therefore, the new method (LDF) has high reliability and could be considered as a procedure that can detect anomaly in earthquake precursor time series.