## 1 Introduction
It can be observed that the ionosphere compared to the total terrestrial size is as thin as the skin of an apple is compared to the whole apple, which is a reason for the strong electromagnetic coupling between the lithosphere and the ionosphere. GNSS satellites orbit the Earth at much higher altitudes but their observations at different frequencies in the L-band are sensitive to the ionosphere, since it acts as a dispersive medium. Starting from the idea that although the underlying processes are not exactly known, there may be sufficient features in the observed ionospheric anomalies to predict large earthquakes in the coming days (until approximately one week later), I want to build a prediction model based on some significant points of consensus already summarized in a previous contribution [26]:  
* Anomalous TEC values are observed from around one week to some hours before a large earthquake with particular signatures that seem to start to be observable above a magnitude of \(M_{w}=6\) (even for \(M_{w}=5\) in some cases), and much more clearly for magnitudes above \(M_{w}=7\)[4; 5; 6; 7; 8; 13; 14; 17; 18; 21; 26; 27; 28]. * The area of the ionosphere in which the anomaly is perceived is usually approximately centered in the vertical of the epicenter [3; 7; 17; 18; 21] due to the injection of ions into the atmosphere subsequent to the tectonic stresses produced beneath, and its extension depends on the magnitude of the earthquake \(M_{w}\) so that its radius is approximately given by the following [3; 26]: \[r=e^{M_{w}}\] (1) where \(e\) is the base of the natural logarithm, or by the equivalent (as demonstrated in [26]) Dobrovolsky formula [14; 17; 18; 21; 29]: \[r=10^{0.43M_{w}}\] (2)  
Although TEC values can be directly estimated from the observations of a GNSS network, in the current work, it was preferred to use Global Ionospheric Maps (GIMs) computed by the International GNSS Service, which are openly distributed to the users in the form of daily files [30; 31]. These GIMs were employed for their reliability, worldwide coverage, and availability for many years, which allows for performing the current long-term analysis. Since TEC variations in both spatial and temporal components are going to be analyzed, the data structures will be 3D matrices, containing the TEC values for the different latitudes and longitudes in the different rows and columns, respectively, and in the third component, the different values for the different times (for example, every 2 h). Deep learning methods, in particular Convolutional Neural Networks (CNN)--which have shown a great performance in image classification, for example [32]--will be used to construct a system with predictive abilities of future large earthquakes using TEC anomalies from GIMs of the preceding days (approximately one week). The rest of the paper is organized as follows. In Section 2, I describe the source of the data and the preparation before they are used by the neural network presented. In particular, it is explained how the three-dimensional matrices (latitude/longitude/time) of the TEC values, along with the subsequent earthquake magnitudes for all large earthquakes that have occurred since the beginning of 2011 until the beginning of October 2024, are obtained (in Sections 2.1 and 2.2, respectively). These data are subsequently prepared in the form of 3D matrices, \(X\), and corresponding scalar values, \(Y\), respectively, and separated into training and validation sets as explained in Section 2.3. The architecture of the neural network used for this study is presented in Section 2.4. The results are presented and discussed in Section 3 and Conclusions are given in Section 4, among which it is highlighted the accuracy of nearly 60% obtained for the validation set. It should be noted that neither the network nor its solution is intended to be optimal due to, among other factors, the limited resources of the author's personal computer with which the computations were performed.