Abstract
A single precursor is not usually an accurate, precise and adequate measure to predict earthquake parameters. Therefore, it is more appropriate to exploit parameters extracted from several other single precursors, so that their simultaneous combinations may reduce the uncertainty of the prediction. In this study, remote sensing observations in different modalities acquired from several days before impending earthquakes have been investigated to extract earthquake parameters. They are observations in electron and ion density, electron temperature, Total Electron Content (TEC), Land Surface Temperature (LST), Sea Surface Temperature (SST), Aerosol Optical Depth (AOD), Surface Latent Heat Flux (SLHF), and Outgoing Longwave Radiation (OLR) clear sky. Regarding the ionospheric precursors, the geomagnetic indices Dst, Kp, Ap and F10.7 were used to detect pre-earthquake disturbances from frequent anomalies associated with geomagnetic activity. In this study, three methods of median, support vector regression (SVR) and random forest (RF) have been used to detect anomalies. When anomalies associated with impending earthquakes are detected, the number of prior days associated with the earthquake is estimated based on the type of precursor. Then, by estimation of the amount of anomaly deviation from the normal state, the magnitude of the impending earthquake is estimated. The final earthquake parameters (such as date and magnitude) can be obtained by integrating the earthquake parameters extracted from different earthquake precursors using mean square error (MSE) method.
Highlights
When an earthquake is happening, energy transmission is generated due the destructive effects of the earthquakes to the environment
Assuming that estimation of Earthquake parameters using each predictor individually is accompanied by some uncertainties, this study considered integrating the capabilities of different earthquake parameters extracted from some of the same 285 earthquake predictors to better estimate earthquake parameters
To identify the anomalous states that may be associated with impending earthquakes, variations of different earthquake precursors have been analysed for four earthquakes by using Median, support vector regression (SVR) and Random Forest methods
Summary
When an earthquake is happening, energy transmission is generated due the destructive effects of the earthquakes to the environment The occurrence of these changes before and/or after the earthquake may have various physical and chemical effects on lithosphere, atmosphere and ionosphere making the earthquake more accurately predictable. The abnormal 25 variations in lithospheric, atmospheric and ionospheric parameters are taken as "earthquake precursors". They serve as alarms for impending earthquakes. By integrating a variety of earthquake parameters extracted from different precursors, a more accurate and suitable estimation of final earthquake parameters may be obtained. Ionospheric anomaly studies include changes in total 40 electron content (TEC) obtained from global positioning receivers (GPS) (Liu et al 2004; Akhoondzadeh 2013; Parrot et al.2016; Tao et al 2017). Other useful precursors are outgoing 45 longwave radiation (OLR) (Ouzounov et al 2007; Rawatet al. 2011; Eleftheriou et al.2016), surface latent heat flux (SLHF) (Dey and Singh 2003; Cervone et al 2004; Cervone et al.2006; Pulinets et al 2006; Pulinets and Ouzounov 2011; Zhang et al 2013; MansouriDaneshvar et al 2014; Qin et al.2014), and atmospheric anomaly in the form of aerosol optical depth (AOD) (Freund et al 2009; Akhoondzadeh, 2015; Ganguly 2016; Akhoondzadeh, 2018)
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