An Information Geometry-Based Method to Study Atmospheric and Seismic Phenomena With VLF Signals

We propose to employ the framework of information geometry to detect anomalies in Very Low Frequency (VLF) and Low Frequency (LF) signal propagation, measured globally across amplitude and phase channels. Using a sliding-window approach, the probability distributions of signal data are compared over adjacent intervals, defining a statistical measure of distinguishability, named information velocity. Information velocity enables the identification of anomalies in VLF/LF signals and connect them to various atmospheric, geophysical, and space weather phenomena. We have shown the effectiveness of information geometry in quantifying signal variability, offering a powerful framework for anomaly detection in VLF data and wider in the geophysical and atmospheric sciences context. The results demonstrate its potential for uncovering insights into ionospheric behavior, atmospheric disturbances, and their interplay with Earth's electromagnetic environment.