Machine Learning Approach for Analysis of Ionosphere Parameters for Earthquake Precursors

Abstract

This research explores the relationship between seismic events and predictive indicators, focusing on machine learning-based strategies for early earthquake prediction. It dissects existing prediction approaches, highlighting ionospheric anomalies' correlation with seismic occurrences, particularly preceding earthquakes of magnitudes higher than 5.5. By identifying a lack of comprehensive long-term analyses in the field, the study emphasizes future trends in machine learning-driven EQ-PD techniques using GPS-TEC data for real-time anomaly detection. The methodology involves seismic hazard monitoring in Turkish coal mines, leveraging specialized equipment and diverse machine-learning algorithms for enhanced prediction accuracy. The study's core involves analyzing seismic wave datasets alongside real-time ionospheric data to evaluate the EQ-PD approach. Utilizing FFT seismic wave analysis, precursor detection, and machine learning-based classification, this research underscores the EQ-PD technique's potential for early earthquake prediction. The findings present a robust framework amalgamating seismic wave analysis, ionospheric anomaly detection, and machine learning, offering promise for practical application in mitigating earthquake impacts.