TimesNet-Gen: Deep Learning-based Site Specific Strong Motion Generation
Effective earthquake risk reduction relies on accurate site-specific evaluations. This requires models that can represent the influence of local site conditions on ground motion characteristics. In this context, data driven approaches that learn site controlled signatures from recorded ground motions offer a promising direction. We address strong ground motion generation from time-domain accelerometer records and introduce the TimesNet-Gen, a time-domain conditional generator. The approach uses a station specific latent bottleneck. We evaluate generation by comparing HVSR curves and fundamental site-frequency f_0 distributions between real and generated records per station, and summarize station specificity with a score based on the f_0 distribution confusion matrices. TimesNet-Gen achieves strong station-wise alignment and compares favorably with a spectrogram-based conditional VAE baseline for site-specific strong motion synthesis. Our codes are available via https://github.com/brsylmz23/TimesNet-Gen.
693344ee3962c926cf680475
Baris Yilmaz
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2025-12-08T08:31:19.869Z
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Yilmaz
Barisylmz
user
Barisylmz
693344ee3962c926cf680476
Bevan Deniz Cilgin
693344ee3962c926cf680477
Erdem Akagündüz
693344ee3962c926cf680478
Salih Tileylioglu
TimesNet-Gen: Deep Learning-based Site Specific Strong Motion Generation
Effective earthquake risk reduction relies on accurate site-specific evaluations. This requires models that can represent the influence of local site conditions on ground motion characteristics. In this context, data driven approaches that learn site controlled signatures from recorded ground motions offer a promising direction. We address strong ground motion generation from time-domain accelerometer records and introduce the TimesNet-Gen, a time-domain conditional generator. The approach uses a station specific latent bottleneck. We evaluate generation by comparing HVSR curves and fundamental site-frequency f_0 distributions between real and generated records per station, and summarize station specificity with a score based on the f_0 distribution confusion matrices. TimesNet-Gen achieves strong station-wise alignment and compares favorably with a spectrogram-based conditional VAE baseline for site-specific strong motion synthesis. Our codes are available via https://github.com/brsylmz23/TimesNet-Gen.
TimesNet-Gen, a time-domain conditional generator with a station-specific latent bottleneck, effectively synthesizes site-specific strong ground motion records, outperforming a spectrogram-based conditional VAE baseline.
TimesNet-Gen time-domain generator latent bottleneck HVSR curves fundamental site-frequency f₀ distribution strong ground motion synthesis conditional VAE