TEC variations analysis concerning Haiti (January 12, 2010) and Samoa (September 29, 2009) earthquakes

Two datasets of total electron content (TEC) time series have been analyzed in this study to locate relevant anomalous variations in recent major Haiti and Samoa Islands earthquakes prior to the events. The duration of two datasets is 45 and 65 days for Haiti and Samoa Islands earthquakes, respectively, each at a time resolution of 2 h. Since long time TEC signals are being used, the analysis excludes the abnormally looking background variations for Haiti and Samoa cases respectively without taking the sources into the considerations. However, some known sources such as solar-terrestrial geomagnetic indices (i.e. D-st and K-p have been used to avoid high natural perturbations which may mask pre-earthquake anomalies. As the first method, the interquartile method has been used to construct the higher and lower bounds in TEC data to detect disturbed states outside the bounds which might be associated with impending earthquakes. As another method, wavelet transformation has been applied on the two time series of TEC data to identify earthquake anomalies. Also in this study, Kalman filter has been applied in the detection process of prominent TEC anomalies related to earthquakes. A cross comparison of the different results concerning the methodology indicates that the interquartile method is capable of detecting the highest intensity anomaly values whereas Kalman filter method significantly detects more anomaly occurrences. All three methods detected a considerable number of anomalous occurrences during 1-15 days prior to the earthquakes in a period of low geomagnetic activities. The proposed method gets its credibility from the overall capabilities of the three integrated methods. In this regard, a good agreement in results was found between the different applied methods on TEC data in the detection of pre-seismic anomalies. The concurrency of the detected anomalous occurrences indicates that these anomalous behaviors are highly related to impending earthquakes. (c) 2010 COSPAR. Published by Elsevier Ltd. All rights reserved.