Toplam Elektron Içeriǧi ve Uzay Iklim Verileri Kullanilarak Transfer Öǧrenme ile Deprem Öncüllerinin Analizi

The stress changes occurring in the Earth's crust before earthquakes may cause some anomalies in the ionosphere and cause significant changes in the Total Electron Content (TEC) and Space Climate (SCC) parameters. The analysis of these changes offers a significant potential in the detection of earthquake precursors. In this study, the analysis of earthquake precursors was performed using TEC and UC data and the effectiveness of transfer learning and machine learning techniques was investigated. Features were extracted from five-day TEC and UC data using a pre-trained Multi-Input One-Dimensional Convolutional Mixer Network (ÇG-TB-EMA) and these features were classified with different machine learning algorithms. Within the scope of the study, earthquakes with a magnitude of Mw≥6.0 that occurred during the low and high solar activity periods of the 24th solar cycle were analyzed. The earthquakes were selected from the Far East region between 10°S-45°N latitudes and 120°E-160°E longitudes, where intense seismic activity is observed. Experimental studies have shown that during the low solar activity period, support vector machines (SVM) and artificial neural networks (ANN) models exhibited the highest performance with 96.04% accuracy and 96.08% F1 score, respectively. During the high solar activity period, the classification success decreased slightly, and SVM and ANN models achieved the best results with 90.28% accuracy and 89.86% F1 score. These findings reveal that the effect of solar activity on ionospheric variables should be taken into account in earthquake precursor detection. For future studies, long-term analyses are recommended to better understand the effects of solar activity.