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<p>Earthquakes pose serious risks to global populations, making early warning systems essential. Conventional methods relying on peak ground displacement and velocity often struggle with the non-stationary nature of seismic signals. This study leverages first-arriving P-wave signals to extract <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\tau _c\)</EquationSource> </InlineEquation>, <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(P_d\)</EquationSource> </InlineEquation> parameters for earthquake event classification and proposes an ensemble-based machine learning architecture that outperforms existing classifiers. While the current state-of-the-art single model achieves 91% accuracy, our approach employs three different ensemble models, and finally proposed model (Architecture-3) delivers 96.56% accuracy, representing a notable improvement with a reduced false alarm rate, accomplishing a false positive rate of 1.96%, enhancing reliability for real-time earthquake early warning (EEW) in the Himalayan region. All models are deployed on the PYNQ-Z2 FPGA platform using IIT Roorkee’s PESMOS data, again our proposed Architecture-3 outperform all, achieving 8.1 ms inference latency, 3.0 W power consumption, and moderate PS utilization (48% CPU, 58% memory), confirming feasibility for real-world implementation. These results highlight the potential of ensemble ML for robust and efficient EEW systems.</p>

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Enhancing Earthquake Preparedness in the Himalayan Region: A Machine Learning Approach using EEW System Parameters

  • 期刊论文

  • 发表日期：

    2026年3月3日

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  • Samik Basu,
  • Sayan Tripathi,
  • Soumen Halder,
  • Arkadip Maitra,
  • Pritha Banerjee,
  • Amlan Chakrabarti

摘要

Earthquakes pose serious risks to global populations, making early warning systems essential. Conventional methods relying on peak ground displacement and velocity often struggle with the non-stationary nature of seismic signals. This study leverages first-arriving P-wave signals to extract \(\tau _c\) , \(P_d\) parameters for earthquake event classification and proposes an ensemble-based machine learning architecture that outperforms existing classifiers. While the current state-of-the-art single model achieves 91% accuracy, our approach employs three different ensemble models, and finally proposed model (Architecture-3) delivers 96.56% accuracy, representing a notable improvement with a reduced false alarm rate, accomplishing a false positive rate of 1.96%, enhancing reliability for real-time earthquake early warning (EEW) in the Himalayan region. All models are deployed on the PYNQ-Z2 FPGA platform using IIT Roorkee’s PESMOS data, again our proposed Architecture-3 outperform all, achieving 8.1 ms inference latency, 3.0 W power consumption, and moderate PS utilization (48% CPU, 58% memory), confirming feasibility for real-world implementation. These results highlight the potential of ensemble ML for robust and efficient EEW systems.