Estimation of the date and magnitude of impending massive earthquakes using the integration of precursors obtainable from remote sensing data

A single precursor is not usually an accurate, precise, and adequate measure to predict earthquake parameters. Therefore, it is more appropriate to combine multiple precursors and exploit parameters extracted from them to reduce the uncertainty of the prediction. The assumption in this study is based on the fact that most Earthquakes happen in active fault zones. The study is about the estimation of Earthquake parameters such as date and magnitude. In this study, remote sensing observations (such as electron and ion density, electron temperature, Total Electron Content (TEC), Land Surface Temperature (LST), Sea Surface Temperature (SST), Aerosol Optical Depth (AOD), and Surface Latent Heat Flux (SLHF)) in different modalities acquired several days before impending earthquakes have been investigated to extract earthquake parameters. In this study, three methods: median, support vector regression (SVR), and random forest (RF) have been used to detect anomalies. Then, by estimating 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 the mean square error (MSE) method.