High Resolution Generative Waveform Modeling Using Denoising Diffusion

Reliable synthesis and prediction of seismic waveforms play an important role in evaluating seismic hazards and designing earthquake-resilient structures. However, current methods, such as ground motion models and physics-based simulations, are often limited in fully capturing the complexity of seismic wave propagation, at higher frequencies (>5 Hz). Some of these limitations can potentially be overcome through machine learning techniques. In earthquake engineering, machine learning models have been used for predicting peak ground accelerations and Fourier spectra responses. To model entire waveforms, extensive efforts to generate seismic waveforms have employed advanced machine learning techniques, such as generative models, with most previous approaches relying on generative adversarial networks (GANs). In contrast to these earlier models, this study presents an efficient and extensible generative framework to produce realistic high-frequency seismic waveforms, compared to GANs. Our approach encodes spectrograms of the waveform data into a lower-dimensional sub-manifold using an autoencoder, and a state-of-the-art diffusion model is subsequently trained to generate these latent embeddings. Conditioning is currently performed on key parameters: earthquake magnitude, recording distance, site conditions, and faulting style. The resulting generative model can synthesize waveforms with frequency content up to 50 Hz, from which several scalar ground motion statistics, such as peak ground motion amplitudes, spectral accelerations, or Arias intensity can be directly derived. We validate the quality of the generated waveforms using standard seismological benchmarks and performance metrics from image generation research. Our openly available model produces high-frequency waveforms that align with real data across a wide range of input parameters, including regions where observations are sparse, and accurately reproduces both median trends and variability of empirical ground motion statistics. Our generative waveform model can be potentially used to perform seismic hazard where broadband data are often required such as to train earthquake early warning model. Given the increasing number of generative waveform models, we emphasize that they should be openly accessible and included in community efforts for ground motion model evaluations.