Speaker
Description
Space weather, particularly solar flares, poses a significant risk to global technological infrastructure, affecting satellites, power grids, communications, and navigation. Very Low Frequency (VLF) signals provide a terrestrial alternative for monitoring space weather: their propagation through the Earth‑ionosphere waveguide is sensitive to D‑region disturbances caused by solar X‑rays, enabling indirect, low‑cost, and real-time detection.
In this preliminary study, we develop and evaluate a Gaussian Hidden Markov Model (HMM) with three hidden states representing the signal's physical states during a flare perturbation (normal, onset, and decay). The model was trained on VLF signals from the NAA transmitter (24 kHz) recorded in Piura, Peru, during 2025. Preprocessing included FIR filtering, downsampling, and manual labeling. The signal was decomposed into background‑removed amplitude and its first derivative (velocity). Critically, the transition matrix was derived from labeled events, imposing physical constraints consistent with solar flare evolution.
Results show that the HMM achieves 71% precision and recall for detecting the onset state, successfully identifying solar events under noisy conditions while maintaining temporal logic aligned with flare physics. The model also demonstrates adequate computational efficiency for real‑time operation.
As future work we consider implementing Gaussian Mixture Model HMMs and implementing an autoregressive HMM.