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Updated: Jun 3, 2025

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Predicting largest expected aftershock ground motions using automated machine learning (AutoML)-based scheme.

Xiaohui Yu1, Meng Wang2, Chaolie Ning2

  • 1College of Civil Engineering and Architecture, Guilin University of Technology, Guilin, China.

Scientific Reports
|January 6, 2025
PubMed
Summary

Automated machine learning (AutoML) accurately predicts aftershock ground motions, crucial for structural safety after earthquakes. This method forecasts seismic demand, improving disaster resilience.

Keywords:
Artificial aftershock ground motionsAutomated machine learning(AutoML)Mainshock-aftershock sequencePeak ductility demandsSpectral accelerations

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Area of Science:

  • Earthquake Engineering
  • Computational Seismology
  • Machine Learning Applications

Background:

  • Aftershocks pose significant risks to structures weakened by mainshocks, necessitating accurate seismic demand assessment.
  • A scarcity of recorded aftershock data hinders the development of reliable synthetic ground motions for structural analysis.
  • Existing methods struggle to capture spectral differences and interdependencies between mainshock and aftershock ground motions.

Purpose of the Study:

  • To develop an innovative approach using automated machine learning (AutoML) to predict the acceleration spectrum (Sa) of the largest expected aftershock.
  • To generate synthetic aftershock accelerograms that accurately represent seismic demand on structures subjected to mainshock-aftershock sequences.
  • To validate the efficacy of AutoML in forecasting aftershock ground motion characteristics with minimal human intervention.

Main Methods:

  • Utilized an AutoML model integrating mainshock parameters (Sa, magnitude, distance) and site characteristics (Vs30).
  • Employed a wavelet-based technique to generate synthetic aftershock accelerograms referencing mainshock ground motion.
  • Trained the AutoML model on a global database of 2500 mainshock-aftershock recordings.

Main Results:

  • The AutoML model achieved high prediction accuracy for aftershock Sa, with R² scores ranging from 0.85 to 0.9 across various periods.
  • Predicted Sa intensities showed strong Pearson correlation with observed aftershock recordings, even without explicit aftershock rupture parameters.
  • Synthetic mainshock-aftershock ground motions generated using the model demonstrated good agreement with peak ductility demands of single-degree-of-freedom systems.

Conclusions:

  • The developed AutoML framework effectively forecasts the response spectrum of major aftershocks, enhancing seismic risk assessment.
  • The automated nature of AutoML allows for potential extension to predict other intensity measures of aftershocks.
  • This approach offers a robust and efficient method for generating realistic synthetic aftershock ground motions, crucial for structural engineering and disaster preparedness.