Earthquake signals are crucial for minimizing the impact of seismic activities. Current algorithms face difficulties in correctly identifying P-waves and assessing magnitudes, which affects the amount of advance warning given. It is crucial to establish standardized methods for the effective selection and integration of multiple algorithms. Machine learning techniques could considerably enhance detection reliability. The research seeks to rectify this shortfall and strengthen automated detection as well as prediction capabilities. The model's performance is assessed using real earthquake data in simulations compared to individual algorithms. The objective of this research is to develop an optimized multi-algorithm framework that enhances the warning lead times and overall reliability. This framework underpinning this method is shaped by the operational demands inherent in early warning systems. The objective of the work is to contribute to the betterment of seismic risk reduction. An ML methodology, merging several distinct detection algorithms, will be deployed along with a tailored prioritization system. The intention is to strengthen the model's dependability and its overall level of consistency. The ML-based multi-algorithm framework significantly boosts the performance of Early Earthquake Warning Systems, providing a scalable approach to enhance automated detection and public safety, ultimately advancing the effectiveness of seismic hazard reduction through quicker and more accurate warnings.
Gupta et al. (Mon,) studied this question.