## 1 Introduction  
The rapid development of artificial intelligence in its various forms, from deep learning to generative artificial intelligence, has made it possible in the last few years to perform tasks that previously seemed out of reach or totally impossible. Its immense potential for accelerating scientific discovery cannot be ignored in any branch of scientific research today. One of the topics considered an impossible task for centuries is the prediction of future earthquakes based on some possible earthquake precursors; however, strange animal behaviors prior to large earthquakes have been documented for many decades, for example [1; 2], and it cannot be ruled out that these anomalous behaviors could be triggered by certain changes in the environment that are perceptible in the Earth's surface, waters, and the atmosphere. Inspired by this idea, some researchers have attained a relative degree of success analyzing certain earthquake precursors. Many successful cases of detection have been published in recent years [3; 4; 5; 6; 7; 8; 9; 10; 11; 12]; however, a method producing consistent results in the long term is still missing. One of the most promising avenues for earthquake prediction is the study of the ionosphere, the ionized part of the atmosphere, which extends from roughly 50 to 1000 km. In relation to the total thickness of the Earth, the analogy could be given that this layer is like the skin of an apple in terms of relative thickness, Figure 1. Ionospheric anomalies, such as abnormal values in the total electron content (TEC) of the ionosphere--a quantity that can be obtained from the processing of Global Navigation Satellite Systems (GNSS) signals--have been shown to occur a few days before several large earthquakes [3; 4; 5; 6; 7; 10]. In the last three decades, the literature has well-documented the fact that electromagnetic perturbations reaching the ionosphere precede major earthquakes by a few days [3; 4; 5; 6; 7; 8; 13; 14; 15; 16; 17; 18]. The physics of the last stages of the earthquake's preparation process within the Earth's crust, and the associated physical and chemical transformations in the area, seem to be the reason for these anomalies. Several studies have described in detail particular electrification mechanisms of mechanically stressed crustal rocks that are compatible with the final observation of electromagnetic anomalies in the ionosphere [16; 19; 20; 21; 22]. The main idea common to these electrification mechanisms is that tectonic stresses cause mobile dislocations in the rocks, which activate electronic charge carriers (positive holes) that also propagate in the surrounding unstressed rocks. When these electronic carriers reach the Earth's surface, they trigger a series of additional reactions that result in ionization of the air, injecting massive amounts of primarily positive ions into the atmosphere. Laboratory tests have also confirmed the plausibility of the mechanism, for example [23; 24]. The generation of these stress-activated electronic charge carriers may also cause ion emission by radon decay; this contributes to air ionization up into the ionosphere and other electric field variations in the biosphere, which could explain the unusual, seemingly premonitory behavior observed in animals [16; 25]. So far, the studies that have examined these anomalies as earthquake precursors are relatively limited in time (a single or few events) or space (a particular geographic area), for example [3; 4; 5; 6; 7; 8; 9; 10]. With these partial successes and the accelerated development of artificial intelligence, one can dream of developing a system for earthquake prediction that consistently delivers the place and magnitude of the forthcoming large earthquakes to happen anywhere on the Earth, much as the weather forecast services. Figure 1: Earth, the ionosphere, and two example GNSS satellites at true scale (except for satellite size).