Environmental Management Hazards 2423-415X 1 1 2014 09 23 Prediction and Detection of Earthquake Ionospheric Anomalies in Total Electron Content of the GIM based on Wavelet Transform Technique and Hazards Reduction (the M 7.7 Saravan Earthquake of April 16, 2013) Prediction and Detection of Earthquake Ionospheric Anomalies in Total Electron Content of the GIM based on Wavelet Transform Technique and Hazards Reduction (the M 7.7 Saravan Earthquake of April 16, 2013) 83 96 52617 10.22059/jhsci.2014.52617 FA Farideh Sabzehee M.Sc. in Geodesy, Department of Surveying and Geomatics Engineering, University College of Engineering, University of Tehran, Iran Mohammad Ali Sharifi Associate Professor, Department of Surveying and Geomatics Engineering, University College of Engineering, University of Tehran, Iran Mehdi Akhoond Zadeh Assistant Professor, Department of Surveying and Geomatics Engineering, University College of Engineering, University of Tehran, Iran Journal Article 2014 11 13 Earthquakes show unknown nonlinear behavior and given the magnitude of the earthquake, we would encounter certain changes in lithosphere, atmosphere and ionosphere. The ionospheric parameters have been found to be sorely susceptible to major earthquakes. In addition to the ionospheric variations generated by solar activity, there are remarkable temporary changes in the ionosphere that are generated by prompt changes in geomagnetic activity. Therefore, recognizing the ionospheric anomaly variations generated by seismic activity or geomagnetic activity is hard, exclusively when there is interposition from little geomagnetic storms. Processing the time series of total electron content (TEC), in order to ionospheric anomalies detection is a significant subject. Two wavelet methods were used to nonlinear and non-stationary time series of the TEC: the analytic wavelet transform (AWT) to detect variation in the TEC, and cross wavelet transform method (XWT) to analyze the mutual relationship between the variability of the ionospheric anomalies and the geophysical indices around the epicenter of the earthquake in the time-frequency domain. The Saravan (28.107˚N, 62.053˚E) earthquake happened on 16 April 2013 during the period of high solar activity in the 24th solar cycle. In this study, we utilized the CODE GIMs from 1 March 2013 to 31 April 2013 for the Saravan earthquake. Under quiet geomagnetic condition, the earthquake was considered the only reason of these changes and within 10 to 15 days before the earthquake and 7 days afterward, severe changes were observed. There was a powerful nonlinear context in the TEC data, generated by abnormal solar irradiance during the studied period. It is essential to eliminate the solar activity and geomagnetic activity traces from the ionospheric TEC to elude for representing error in the TEC time series. To recognize if the ionospheric perturbation detected by the AWT is connected to geomagnetic activity, we carried out the XWT for the TEC and AP time series from 1 March to 31 April 2013. It specifies that there is one common high energy region extract within the two time series. The common high energy region related to 17 March 2013. Accordingly, this increment was more probably caused by the geomagnetic storm effects. Within the dynamic range of earthquake, no energetic common point was observed which showed that geomagnetic activity had no role in ionospheric anomalies and another factor, very probably the earthquake was the root of the mentioned anomalies. Therefore, in order to reduce hazard, given TEC time series, the time and frequency of the earthquake could be predicated and defined by evaluating ionospheric parameters. Earthquakes show unknown nonlinear behavior and given the magnitude of the earthquake, we would encounter certain changes in lithosphere, atmosphere and ionosphere. The ionospheric parameters have been found to be sorely susceptible to major earthquakes. In addition to the ionospheric variations generated by solar activity, there are remarkable temporary changes in the ionosphere that are generated by prompt changes in geomagnetic activity. Therefore, recognizing the ionospheric anomaly variations generated by seismic activity or geomagnetic activity is hard, exclusively when there is interposition from little geomagnetic storms. Processing the time series of total electron content (TEC), in order to ionospheric anomalies detection is a significant subject. Two wavelet methods were used to nonlinear and non-stationary time series of the TEC: the analytic wavelet transform (AWT) to detect variation in the TEC, and cross wavelet transform method (XWT) to analyze the mutual relationship between the variability of the ionospheric anomalies and the geophysical indices around the epicenter of the earthquake in the time-frequency domain. The Saravan (28.107˚N, 62.053˚E) earthquake happened on 16 April 2013 during the period of high solar activity in the 24th solar cycle. In this study, we utilized the CODE GIMs from 1 March 2013 to 31 April 2013 for the Saravan earthquake. Under quiet geomagnetic condition, the earthquake was considered the only reason of these changes and within 10 to 15 days before the earthquake and 7 days afterward, severe changes were observed. There was a powerful nonlinear context in the TEC data, generated by abnormal solar irradiance during the studied period. It is essential to eliminate the solar activity and geomagnetic activity traces from the ionospheric TEC to elude for representing error in the TEC time series. To recognize if the ionospheric perturbation detected by the AWT is connected to geomagnetic activity, we carried out the XWT for the TEC and AP time series from 1 March to 31 April 2013. It specifies that there is one common high energy region extract within the two time series. The common high energy region related to 17 March 2013. Accordingly, this increment was more probably caused by the geomagnetic storm effects. Within the dynamic range of earthquake, no energetic common point was observed which showed that geomagnetic activity had no role in ionospheric anomalies and another factor, very probably the earthquake was the root of the mentioned anomalies. Therefore, in order to reduce hazard, given TEC time series, the time and frequency of the earthquake could be predicated and defined by evaluating ionospheric parameters. https://jhsci.ut.ac.ir/article_52617_87b2866c531a70fef4a171d55b909548.pdf