## 3 Results and Discussion  
The neural network has been trained with the training variables mentioned in Section 2.3, which are available for the readers in Supplementary Materials S1 and S2, respectively (Xtraining and Ytraining). They comprise 80% of the earthquake-related information for the period under study (from 1 January 2011 to 1 October 2024). The remaining 20% of the information has been used for validation, these are the validation variables mentioned in Section 2.3, which are available for the readers in Supplementary Materials S3 and S4, respectively (Xvalidation and Yvalidation). As acknowledged in Section 2.2, the number earthquakes of the smallest magnitudes (mainly those below \(M_{w}\) 6.0) have been reduced to provide a more manageable dataset; first, because their representation in the dataset is still very abundant, and second, because following the expressions in use, Equations (1)-(4), the areas affected by earthquakes of these magnitudes are comparatively much smaller than those of the largest magnitudes. Please remember, in this regard, anomalies that could be triggered by events of magnitudes below \(M_{w}\) 5.5 are considered in the same group as calm periods (refer to group number 1 in Table 1) since this research is mainly aimed at the potential detection of very large earthquakes. The training and validation process was performed using the Matlab Deep Learning Toolbox [38] on a standard personal computer; Figure 6 shows its evolution. The Stochastic Gradient Descent with Momentum optimizer has been used to update the weights. It has a validation frequency of 5, which helps maintain a strong link between the validation data and training data without excessively increasing the computational burden of validation, and a minibatch size of 100, which is a value in the mid-size range commonly used for image classification. A relatively large number of epochs (150) and iterations (4500 in total, 30 per epoch) have been selected, although the accuracy of both the training and validation sets tend to stabilize a bit after reaching the middle of the calculation. Until then, a fairly gradual increase in accuracy for both the training and the validation sets is observed. After 150 epochs, a validation accuracy of 59.70% is achieved. This is a greater success than it may seem since first, having eight categories (those in Table 1), the incorrect prediction of an earthquake with a neighboring category could also serve for the purpose of a warning system, and, second, because the magnitude values close to the interval limits (for example, a magnitude value of \(M_{w}\) 6.2, which belongs to category 2 but is very close to belonging to category 3, see Table 1) can be, almost equally well, attributed to both categories. Figure 5: Scheme of the network layers. Overall, the confusion matrix obtained for the whole dataset is shown in Figure 7, where the color intensity gives an indication of the reliability according to the number of instances in each classification: in different intensities of blue for diagonal values, which refer to correct classification, and in different intensities of red for off-diagonal values, which imply misclassification and are rather pale in this case, with darker values indicating a higher number of classification errors. As can be seen, the diagonal values clearly dominate the matrix, with adjacent categories having the second main importance. It must be considered that in the current  
Figure 6: Evolution of the network training. Figure 7: Confusion matrix for the eight categories (here named with the central \(M_{w}\) values) as defined in Table 1.  
implementation, the attributed category is the one to which the network attributes a higher probability. Other alternatives, such as performing a weighted average considering the probabilities of the different classes, could also be explored in the future. Please remember that a single event (like the only one with a magnitude \(M_{w}\) above 9) can be predicted several days before (not only one day before).