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.