Abstract
In the present paper, we analyze the GPS-TEC data which observed at Agra station, India (27.2o N, 78o E) and also see the diurnal variations of global ionospheric maps (GIMs) TEC data over the epicenter of this earthquake (M=7.8) in Pakistan region for the month of April, 2013. We use a statistical technique for the analysis of data and identify the significant precursors using 3σ criterion. These precursors are found in form of enhancements on different days in the interval of 5-7 days prior to the earthquake. We also see the effect of geomagnetic storm and solar activity on TEC data and found no anomaly associated with them.
Highlights
In the last few decades, researchers have presented several results to predict the electromagnetic phenomenon [1,2]
Liu et al [9] have analyzed the global ionospheric maps (GIM) and GPS data of total electron content (TEC) to see the anomalous variation associated with 35 M ≥ 6.0 earthquakes that occurred in China using a statistical based quartile analysis for the period of 10 years from 1 May 1998 to 30 April 2008 and shows that the GPS-TEC decreased above the epicenter on 3-5 days before 17 M ≥ 6.3 earthquakes
Kon et al [11] have used a statistical superimposed epoch analysis (SEA) to analyze the GIM data for the period of 12 years and consider the large number earthquakes even low magnitudes, the results show 1-5 days of precursory period
Summary
In the last few decades, researchers have presented several results to predict the electromagnetic phenomenon [1,2]. Liu et al [9] have analyzed the global ionospheric maps (GIM) and GPS data of TEC to see the anomalous variation associated with 35 M ≥ 6.0 earthquakes that occurred in China using a statistical based quartile analysis for the period of 10 years from 1 May 1998 to 30 April 2008 and shows that the GPS-TEC decreased above the epicenter on 3-5 days before 17 M ≥ 6.3 earthquakes. Further they have shown the anomalous variations during the Wenchuan earthquake of magnitude Mw=7.9 on 12 May 2008. Discussion proves that statistical studies may be consider as the most dominant tool for the study of earthquakes precursors
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