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Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
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Learning earthquake ground motions via conditional generative modeling.

Pu Ren1, Rie Nakata2,3,4, Maxime Lacour4,5

  • 1Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. ren.pu@northeastern.edu.

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|March 17, 2026
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Summary
This summary is machine-generated.

An artificial intelligence (AI) spectrogram generator, Conditional Generative Modeling for Ground Motion (CGM-GM), predicts seismic ground motions. This AI tool shows promise for improving seismic hazard assessment and infrastructure resilience.

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Area of Science:

  • Geophysics
  • Artificial Intelligence
  • Seismology

Background:

  • Predicting earthquake ground motions is vital for seismic hazard assessment and infrastructure resilience.
  • Current methods like empirical simulations and physics-based models have limitations due to data sparsity, computational intensity, and complex Earth structure requirements.

Purpose of the Study:

  • To introduce an AI-driven spectrogram generator, Conditional Generative Modeling for Ground Motion (CGM-GM), for high-fidelity ground motion prediction.
  • To leverage AI for capturing spatially continuous Fourier amplitude spectra (FAS) and waveform properties without explicit physics constraints.

Main Methods:

  • Utilized a probabilistic autoencoder to extract latent time-frequency distributions.
  • Employed variational sequential models for prior and posterior distributions.
  • Inputted earthquake magnitudes and geographic coordinates into the Conditional Generative Modeling for Ground Motion (CGM-GM) model.

Main Results:

  • The Conditional Generative Modeling for Ground Motion (CGM-GM) model successfully captured spatially continuous Fourier amplitude spectra (FAS).
  • The model accurately predicted seismic properties like P and S arrivals and waveform durations.
  • Evaluated using San Francisco Bay Area earthquake records, demonstrating effective performance.

Conclusions:

  • Conditional Generative Modeling for Ground Motion (CGM-GM) shows potential as a complementary tool to physics-based simulations and empirical ground motion models.
  • The AI approach offers a promising advancement in seismology for seismic hazard assessment and infrastructure resilience.