Artificial Intelligence for Earthquake Prediction: A Preliminary System Based on Periodically Trained Neural Networks Using Ionospheric Anomalies

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There is increasing evidence that anomalies in the ionosphere could appear a few days before large earthquakes. Many significant successes with using anomalies for predictions have been reported, although they are usually limited, both in space, to a specific geographic area, and in time, to one or a few events. To date, no solution has been presented that consistently yields the location and magnitude of future earthquakes and thus can be used to develop a warning service. The purpose of this research is to improve on the possible use of Global Ionospheric Maps for earthquake prediction. The use of three-dimensional data matrices, having spatiotemporal information to feed a convolutional neural network, is proposed in this contribution. This network was trained on all large earthquakes occurring from the beginning of the year 2011 to the beginning of October 2024 but it is proposed that it be periodically retrained with new data. This network has reached an accuracy of around 60% in the validation data for a division into eight categories of different earthquake magnitudes. Nevertheless, this percentage increases considerably if the classification into neighboring categories is also accepted, something that could be clearly admissible for the purposes of a warning system. The author believes that success in this endeavor has to come from a collaborative effort. For this reason, the training and validation data with three-dimensional matrices (latitude/longitude/time) of total electron content values along with the subsequent earthquake magnitudes are provided in this paper along with the trained network. Researchers are strongly encouraged to improve on the current neural network with or without the inclusion of additional information.