Synthetic Seismic Accelerogram Generation via Wavelet-Decomposed Conditional Generative Adversarial Networks

The generation of synthetic seismic accelerograms is a critical problem in earth- quake engineering, where the scarcity of strong-motion records, particularly for high-magnitude and near-fault scenarios, limits the reliability of structural analyses and probabilistic seismic hazard assessments. This paper proposes a wavelet-decomposed conditional Generative Adversarial Network (WD-cGAN) for the synthesis of seismic accelerograms that faithfully reproduce the phys- ical and statistical properties of real ground-motion records. Unlike prior GAN-based approaches that rely on Fourier-domain decomposition, the pro- posed architecture decomposes each training signal into N wavelet sub-bands (experimentally N ∈ {5, 6}) using the Daubechies-4 (db4) discrete wavelet transform (DWT), assigning each sub-band to a dedicated discriminator. A novel energy-based weighting scheme αi modulates the relative contribution of each discriminator to the total generator loss, ensuring that physically dominant, low-frequency bands, which carry the bulk of seismic energy, receive proportionally higher training emphasis. Seismic moment magnitude Mw serves as the primary conditioning variable, enabling targeted synthesis for specific hazard scenarios. The model is implemented in Python using PyTorch and trained on accelerograms drawn from the Italian INGV/ITACA v4.0 archive. Qualitative evaluation confirms that the proposed wavelet-domain multi-discriminator scheme improves the realism and physical consistency of synthetic accelerograms relative to a single-discriminator baseline; full quantitative validation on a larger corpus is identified as the principal avenue for future work.