Kalman Filter fusion of satellite seismo-LAI precursors for predicting the magnitude of an impending powerful earthquake

The requirement for creating a robust and efficient earthquake warning system is to estimate the parameters of the upcoming earthquake (location, time and magnitude) with low uncertainty. This goal is achieved by increasing the number of observational earthquake precursors. Today, satellite remote sensing has made it possible to observe the behaviour of a large number of LAI (Lithospheric Atmospheric Ionospheric) precursors. Next, it is necessary to estimate the final desired earthquake parameters by using different fusion methods and combining the earthquake parameters estimated by different precursors. In this study, using the Kalman filter fusion-based method and analysing different LAI precursors related to three powerful earthquakes including: Turkey (6 February 2023), Myanmar (28 March 2025) and Russia-Kamchatka (9 July 2025), the magnitude parameter of the mentioned earthquakes is estimated. Then, the obtained results are compared with the real observed magnitude value and also three fusion methods including Fuzzy Inference System (FIS), Dampsfer Schafer (DS) and Bayesian. The results obtained show that the proposed Kalman filter fusion method, along with other fusion methods, can be used effectively in predicting earthquake parameters.