A decision-making machine learning based approach for earthquake early warning Mukesh Gupta1

Earthquake early warning systems have become vital for minimizing damage from seismic events. However, their automated detection capabilities need strengthening to provide real-time alerts. Current algorithms have limitations in identification of P-waves and magnitude estimation, impacting warning lead times. Additionally, existing single-algorithm dependent systems are prone to errors. There is a need for standardized practices to optimally select and combine algorithms. Machine learning and artificial intelligence show promise to make detection more robust. Models trained on diverse seismological data can learn complex patterns to detect emergent P-waves earlier and refine magnitude assessment. However, research exploring such data driven approaches within early warning systems is limited. This study aims to address this research gap and strengthen automated detection capabilities. It proposes a machine learning model integrating multiple existing algorithms using a novel prioritization framework. Performance is evaluated on real earthquake datasets through simulations vis-à-vis single algorithms. By developing an optimized multi-algorithm framework, this study seeks to improve warning lead times and reliability. The model is designed considering operational requirements of early warning systems. Comparison of results with past methods helps evaluate contributions to the field. Overall, the research strives to enhance seismic hazard mitigation through more efficient automated detection in early warning networks.