Optimising the Uttarakhand EEWS: A Hybrid Data and Next-Generation Algorithm Approach

The Uttarakhand Himalaya, situated in the central seismic gap, is one of India’s most active earthquake zones. Although a state-specific Uttarakhand Earthquake Early Warning System (UEEWS) is currently operational, its dependence on generic magnitude scaling relations and the conventional STA/LTA algorithm for P-wave detection leaves room for enhancement in accuracy and speed—especially given the complex tectonic and site conditions of the Garhwal and Kumaon regions. This study presents a two-pronged strategy to strengthen the UEEWS. First, we develop region-specific magnitude scaling relations using a mixed dataset of observed and simulated seismograms, thereby reducing real-time magnitude estimation uncertainties by accounting for local attenuation and source properties. Second, we propose APPNA (Auto Picking of P-wave Onset using Next-Gen Algorithm), a novel computational method designed to improve onset detection accuracy, increase noise resilience, and reduce false triggers compared to the STA/LTA approach. Validated on both real and synthetic data, these advancements demonstrate that integrating tailored scaling relations with an improved picking algorithm can significantly optimize the performance of an earthquake early warning system in high-hazard regions. Our findings underscore the potential of leveraging UEEWS data, regionally calibrated relations, and innovative algorithms like APPNA to enhance the operational effectiveness of the Uttarakhand warning system