TimesNet-Gen: Deep Learning-based Site Specific Strong Motion Generation

Effective earthquake risk reduction relies on accurate site-specific evaluations, which require models capable of representing 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 TimesNet-Gen, a time-domain conditional generator. The proposed approach employs a latent bottleneck with station identity conditioning. Model performance is evaluated by comparing horizontal-to-vertical spectral ratio (HVSR) curves and fundamental site frequency ($f_0$) distributions between real and generated records on a station-wise basis. Station specificity is further summarized using a score derived from confusion matrices of the $f_0$ distributions. The results demonstrate strong station-wise alignment and favorable comparison with a spectrogram-based conditional variational autoencoder baseline for site-specific strong motion synthesis. The code will be made publicly available after the review process. Our codes are available viathis https URL.