Hybrid Approach of Real-time Ground Motion Prediction for Earthquake Early Warning

Authors

  • Hongcai Zhang Fujian Earthquake Agency, China Earthquake Administration, Fuzhou, China
  • Qifang Liu Suzhou University of Science and Technology, Suzhou, China
  • Xing Jin Fujian Earthquake Agency, China Earthquake Administration, Fuzhou, China | Institute of Xiamen Marine Seismology, China Earthquake Administration, Xiamen, China
  • Huiteng Cai Fujian Earthquake Agency, China Earthquake Administration, Fuzhou, China
  • Zhiyong Chen Fujian Earthquake Agency, China Earthquake Administration, Fuzhou, China
  • Shicheng Wang Fujian Earthquake Agency, China Earthquake Administration, Fuzhou, China

DOI:

https://doi.org/10.26443/seismica.v5i1.2194

Abstract

We propose a hybrid approach for ground motion field prediction in earthquake early warning (EEW) systems, combining the Propagation of Local Undamped Motion (PLUM) method with advanced machine learning (ML) algorithms. Our methodology integrates PLUM's seismic wavefield estimation with CONIP, a four-layer Convolutional Neural Network (CNN) model for onsite seismic intensity estimation, enabling real-time ground motion field predictions. Trained on 69,089 KiK-net seismic records, the ML-based model demonstrates a 28.8% reduction in median absolute error (MAE) in predicted Japan Meteorological Agency (JMA) seismic intensity compared to the traditional ground motion prediction equation (GMPE) implementation, with predictions available 1-5 seconds earlier during the initial 10 seconds after origin time. These improvements reflect enhanced accuracy in estimating peak ground motion intensities and greater stability in early-stage predictions. By employing a Monte Carlo simulation with Gaussian-perturbed inputs, the model yields well-calibrated uncertainty estimates that correlate with prediction errors. When benchmarked against the GMPE-based method, our approach shows consistent performance advantages in prediction reliability. Offline simulations of the 2005 West off Fukuoka earthquake confirm the hybrid approach’s computational efficiency and enhanced predictive capability, particularly for destructive events. By improving prediction accuracy at critical thresholds, this solution provides an average lead time gain of 5.49 seconds over the original PLUM method for end-users and strengthens EEW effectiveness.

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2026-05-24

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Zhang, H., Liu, Q., Jin, X., Cai, H., Chen, Z., & Wang, S. (2026). Hybrid Approach of Real-time Ground Motion Prediction for Earthquake Early Warning. Seismica, 5(1). https://doi.org/10.26443/seismica.v5i1.2194

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Copyright (c) 2026 Hongcai Zhang, Qifang Liu, Xing Jin, Huiteng Cai, Zhiyong Chen, Shicheng Wang

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