Ground Motion Simulation Via Generative Adversarial Network
Kai Chen, Hua Pan,*, Meng Zhang, Zhi-Heng Li
1. Institute of Geophysics,China Earthquake Administration Beijing 100081, China
2. Chongqing Earthquake Agency Chongqing 401147, China
3. Shandong Earthquake Agency Jinan 250014, China
Abstract：
This study addresses the pressing challenge of generating realistic strong ground motion data for simulating earthquakes, a crucial component in pre-earthquake risk assessments and post-earthquake disaster evaluations, particularly suited for regions with limited seismic data. Herein, we report a generative adversarial network (GAN) framework capable of simulating strong ground motions under various environmental conditions using only a small set of real earthquake records. The constructed GAN model generates ground motions based on continuous physical variables such as source distance, site conditions, and magnitude, effectively capturing the complexity and diversity of ground motions under different scenarios. This capability allows the proposed model to approximate real seismic data, making it applicable to a wide range of engineering purposes. Using the Shandong Pingyuan earthquake as an example, a specialized dataset was constructed based on regional real ground motion records. The response spectrum at target locations was obtained through inverse distance–weighted interpolation of actual response spectra, followed by continuous wavelet transform to derive the ground motion time histories at these locations. Through iterative parameter adjustments, the constructed GAN model learned the probability distribution of strong-motion data for this event. The trained model generated three-component ground-motion time histories with clear P-wave and S-wave characteristics, accurately reflecting the non-stationary nature of seismic records. Statistical comparisons between synthetic and real response spectra, waveform envelopes, and peak ground acceleration show a high degree of similarity, underscoring the eff ectiveness of the model in replicating both the statistical and physical characteristics of real ground motions. These findings validate the feasibility of GANs for generating realistic earthquake data in data-scarce regions, providing a reliable approach for enriching regional ground motion databases. Additionally, the results suggest that GAN-based networks are a powerful tool for building predictive models in seismic hazard analysis.
作者简介: Chen Kai, a Senior Engineer at the Chongqing Earthquake Agency and a PhD student specializing in engineering seismology at the Institute of Geophysics, focuses on ground motion simulation and researching seismic activity.
2025, Vol. 22 
