MaxEnt SeismoSense Model: Ionospheric Earthquake Anomaly Detection Based on the Maximum Entropy Principle

Abstract

In our exploration, we aimed at identifying seismic anomalies using limited ionospheric data for earthquake forecasting and we meticulously compiled datasets under conditions of minimal geomagnetic disturbance. Our systematic evaluation affirmed the ITransformer as a potent tool for the feature extraction of ionospheric data, standing out within the domain of transformer-based time series prediction models. We integrated the maximum entropy principle to fully leverage the available information, while minimizing the influence of presuppositions on our predictions. This led to the creation of the MaxEnt SeismoSense Model, a novel composite model that combines the strengths of the transformer architecture with the maximum entropy principle to improve prediction accuracy. The application of this model demonstrated a proficient capability to detect seismic disturbances in the ionosphere, showcasing an improvement in both recall rate and accuracy to 71% and 69%, respectively, when compared to conventional baseline models. This indicates that the combined use of transformer technology and the maximum entropy principle could allow pre-seismic anomalies in the ionosphere to be sensed more efficiently and could offer a more reliable and precise approach to earthquake prediction.