Investigation of the application of geospatial artificial intelligence for integration of earthquake precursors extracted from remotely sensed SAR and thermal images for earthquake prediction.

The main factors contributing to the occurrence of an earthquake are under the crust. Also; due to the lack of access to direct measurements of these factors and the parameters involved in the occurrence of an earthquake, the main goal of researchers is to study the earthquake occurrence through its precursors. Currently, monitoring and identifying some of these precursors are made possible by geomatics technologies. It is an undeniable fact that the behavioral variations of the precursors don't follow a common pattern in all earthquakes. Also, the variations of the precursors show peculiar behaviors in each region. So, it seems infeasible to provide an accurate prediction based on the analysis of the behavioral variations of a single precursor. Unlike previous studies, this study doesn't have a single-parametrical orientation toward an earthquake prediction process. Accordingly, this study aims to extract the trend of variations in crustal deformation anomalies and thermal anomalies before the earthquake to analyze them through an integrated process based on data mining methods. As a result, the tests of earthquake predictions for 17 cases have shown that the proposed method can make a reliable prediction of the probable time and magnitude range of oblique-thrust earthquakes with a magnitude greater than 5.5. Moreover, the proposed method has been able to accurately estimate the occurrence of the 26th November 2019 Albania earthquake (Mw = 6.4) as well as 21th September 2019 Albania earthquake (Mw = 5.6) before they happen. [ABSTRACT FROM AUTHOR]