Integrating Solar Activity and Geomagnetic Disturbance Techniques with Neural Networks for Geophysical Event Prediction: Insights from Seismic Analysis

Abstract

Recent advances in machine learning, particularly neural networks, have paved the way for innovative approaches to predicting geophysical phenomena. This study explores the integration of solar activity data with neural network methodologies to classify and predict seismic events and geomagnetic disturbances. Two case studies were utilized: the classification of seismic events influenced by solar activity using a Long Short-Term Memory (LSTM) model, and geomagnetic disturbance predictions via K-index classification employing neural networks.The first case study utilized proton density data from the Solar and Heliospheric Observatory (SOHO) and seismic records from the U.S. Geological Survey (USGS). The LSTM model achieved an accuracy of 84.47% in classifying seismic events, highlighting the significance of proton density variations as precursors to seismic activities. Weighted learning techniques addressed data imbalance, enabling accurate classification of rare seismic occurrences.In the second case study, geomagnetic data from the Almaty Geomagnetic Observatory was analyzed. A neural network model optimized for K-index classification achieved a remarkable 98% accuracy, demonstrating the robustness of neural architectures in space weather prediction. Temporal dependencies and diurnal cycles in geomagnetic disturbances were captured effectively, underscoring the utility of advanced machine learning techniques in understanding Earth's magnetic environment.The combined findings affirm the potential of integrating solar activity data with neural network frameworks for geophysical forecasting. This approach not only enhances disaster preparedness but also contributes to the theoretical understanding of the interplay between solar and terrestrial dynamics. Future research should focus on extending these methodologies to broader datasets and incorporating additional physical parameters for improved predictive reliability.

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