The research focuses on exploring the potential of Very Low Frequency (VLF) signals as indicators of seismic and geomagnetic activities, particularly in the context of earthquake prediction. It delves into the analysis of VLF data using wavelet-based methodologies and neural networks to detect anomalies preceding seismic events.Observations and analysis of VLF data revealed fluctuations and discrepancies, notably "silent days," occurring several days before seismic events. These irregularities studied through wavelet analysis, established the VLF signals' capability to serve as predictive markers by computing model signals and identifying deviations in amplitude and phase.The study integrated wavelet analysis with neural networks, employing a multi-layer perception model, showcasing promise in recognising anomalous VLF signals. This approach demonstrated the connection between recorded VLF amplitude and phase anomalies at the Petropavlovsk-Kamchatski station and their correlation with seismic and geomagnetic activity, notably during the primary and recovery stages.By implementing deep residual techniques, the research provided more profound insights into VLF signal behaviours before, during, and after seismic events. It facilitated identifying and differentiating between disturbed and undisturbed VLF/LF signals, enhancing the earthquake precursor database.The findings underscore the potential of VLF signals as a crucial tool for earthquake prediction, particularly emphasising deviations in mean values and variances before prognostic days. Interestingly, disruptions in VLF signal characteristics during calm periods, devoid of seismic and geomagnetic activity, suggest their sensitivity to environmental changes.In summary, this research showcases the feasibility of leveraging VLF signals, using wavelet-based methodologies and neural networks, to detect anomalies preceding seismic and geomagnetic events early. While promising, the study emphasises the need for continued exploration and refinement of these techniques to establish robust earthquake precursor identification and prediction models. The ongoing investigation into VLF signal behaviours, their correlation with seismic and geomagnetic activities, and their integration into predictive frameworks will significantly advance early warning systems for seismic events.
