Ground motion model for acceleration response spectra using conditional-generative adversarial network

Pavan Mohan Neelamraju (), Jahnabi Basu () and S. T. G. Raghukanth ()
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Pavan Mohan Neelamraju: Indian Institute of Technology Madras
Jahnabi Basu: Indian Institute of Technology Madras
S. T. G. Raghukanth: Indian Institute of Technology Madras

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 4, No 45, 4865-4900

Abstract: Abstract The present study focuses on developing a ground motion model (GMM) for 5%-damped spectral acceleration (Sa) using a Conditional Generative Adversarial Network (C-GAN). Unlike traditional methods, the model incorporates the physics of source, path, and site characteristics into the adversarial training process between the generator and discriminator. The model is trained on a comprehensive dataset comprising 23,929 ground motion records from both horizontal and vertical directions, sourced from 325 shallow crustal events in the updated NGA-West2 database. The input parameters include the moment magnitude (Mw), Joyner-Boore distance (RJB), the focal mechanism (F), hypocentral depth (Hd), average shear-wave velocity up to 30 m depth (Vs30), and the direction of Sa (dir). To ensure the model’s integrity, an inter-event and intra-event residual analysis is conducted, validating its robustness and unbiasedness. Additionally, the model’s performance is evaluated against established GMMs relevant to similar seismo-tectonic backgrounds. Moreover, the applicability of the developed model is demonstrated through the estimation of site-specific response spectra for Chi-Chi, Taiwan and Loma Prieta. Thus, the study contributes to advancing ground motion modelling techniques applicable in seismic hazard assessment and structural engineering practices.

Keywords: C-GAN; Ground motion prediction; NGA-West2; GMM; Response spectra (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-024-06988-1

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