Generative AI Learns to Synthesize Earthquake Ground Motions That Match Target Spectra

Earthquake engineers have long faced an awkward compromise. When they need realistic shaking records to test how buildings, bridges, and dams will respond in a future quake, they can either use real recordings, which are scarce and rarely match the hazard level required for a specific site, or they can generate artificial accelerograms, which hit the target response spectrum but often look suspiciously synthetic. A new study published in the Bulletin of Earthquake Engineering by Xiaohu Hu, Su Chen, and colleagues at Beijing University of Technology, the Institute of Geophysics of the China Earthquake Administration, Jianghan University, and East China Jiaotong University argues that generative artificial intelligence can finally dissolve that trade-off. The team built a spectrum-conditioned generative model, called STFT-VAEGAN, that produces acceleration time histories whose response spectra conform to a prescribed target while preserving the irregular, nonstationary texture that makes real ground motion look and behave like real ground motion.

The response spectrum is the central currency of earthquake engineering. It summarizes, for a range of vibration periods, the peak response a single-degree-of-freedom oscillator would experience when subjected to a particular accelerogram, and design codes around the world specify shaking demands in exactly these terms. A spectrum-compatible time history is therefore a record whose computed response spectrum matches a code spectrum or a scenario-specific prediction across the period range of interest. Classical approaches to this problem, including wavelet adjustment and frequency-domain optimization, iteratively tweak an existing record until its spectrum fits. Those methods work, but they can distort the phase characteristics, duration, and energy distribution of the original motion, sometimes producing signals that are mathematically compliant yet physically implausible, with unrealistic pulses or smeared energy that mislead nonlinear structural analyses.

The new framework attacks the problem in the time-frequency domain. The authors convert ground motion records into short-time Fourier transform, or STFT, representations, which describe how the amplitude content of the signal evolves across both time and frequency. This representation is crucial because earthquake shaking is inherently nonstationary: high-frequency P- and S-wave energy arrives first, lower-frequency surface waves dominate later, and the amplitude envelope rises and falls in ways that strongly affect structural response. By training a generative model on STFT amplitudes conditioned on a target response spectrum and a latent random variable, the model learns the conditional distribution of time-frequency amplitudes that real, spectrum-consistent accelerograms occupy. Once trained, it can sample that distribution, drawing an effectively unlimited family of distinct accelerograms for any given target spectrum, each with its own plausible waveform realization.

The architecture combines two of the most influential ideas in modern generative machine learning. A variational autoencoder provides a structured latent space from which diverse samples can be drawn, while a generative adversarial network, trained with the Wasserstein distance and a gradient penalty, sharpens the realism of the generated time-frequency amplitudes by forcing them to be statistically indistinguishable from those of genuine records. The Wasserstein formulation, introduced by Arjovsky and colleagues and refined by Gulrajani and colleagues, was chosen for its well-behaved gradients, and the authors report that training histories showed no numerical divergence and no abrupt collapse of sample diversity, a failure mode that plagues conventional adversarial training. The Griffin-Lim style signal reconstruction then maps the generated amplitude information back into the time domain to yield acceleration time histories.

The most distinctive component, however, is what the authors call the response spectrum consistency network, or RSCN. Rather than hoping that spectrum compatibility emerges implicitly from training on real records, the team embedded an explicit spectrum consistency loss directly into the training objective. The RSCN acts as a differentiable surrogate for the computation of the response spectrum, allowing gradients to flow from a period-weighted measure of spectral mismatch back into the generator. In ablation experiments on an event-independent test subset, adding the RSCN measurably reduced response spectrum residuals and improved period-specific calibration compared with an otherwise identical model trained without it. In other words, the network does not merely imitate the spectral statistics of its training data; it actively learns to hit the specific target spectrum handed to it at generation time.

Where do those target spectra come from? The study couples the generative model with a validation-derived, period-weighted ensemble ground motion model, or GMM, that supplies scenario-dependent targets. Ground motion models, the empirical equations that predict spectral accelerations as a function of magnitude, distance, and site conditions, have themselves been undergoing a machine-learning transformation, with neural networks, XGBoost, and hybrid recurrent architectures increasingly used to capture regional attenuation and site effects. By ensembling such predictions and weighting them across the period range, the framework produces a target spectrum tailored to a specific earthquake scenario, and the STFT-VAEGAN then fills in the missing physics: the full stochastic waveform structure that a spectrum alone cannot specify. This linkage between scenario-based spectrum prediction and stochastic time history generation is, according to the authors, the central contribution of the work.

The training and validation data were drawn from the NGA-West2 strong-motion database, the peer-maintained compilation of shallow crustal earthquake recordings curated at the Pacific Earthquake Engineering Research Center, which has become the de facto standard for empirical ground motion modeling worldwide. The authors evaluated the model on a test subset held out by event, a stricter protocol than holding out individual records, because it prevents the network from memorizing the characteristics of particular earthquakes. Test examples demonstrated that the generated accelerograms carry response spectra compatible with their targets while retaining nonstationary time-frequency structure. The team also applied the framework in a case study of the Chi-Chi earthquake, one of the most extensively recorded events in modern seismology, showing that the model can produce multiple distinct accelerograms appropriate to that scenario.

The study builds on a rapidly growing body of work applying deep generative models to seismology. Earlier efforts include conditional GANs that synthesize nonstationary ground motion in the time-frequency domain, data-driven broadband synthesis of earthquake records, and adversarial neural operators for broadband ground-motion synthesis. Hu and colleagues had previously developed a conditional variational autoencoder with adversarial training for spectrum-compatible artificial accelerograms, published in Engineering Structures, and the new Bulletin of Earthquake Engineering paper extends that line by integrating the explicit spectrum consistency loss and the ensemble ground motion model into a single end-to-end pipeline. The distinction matters for practice: a generator that can accept an arbitrary, scenario-derived target spectrum and return a family of conforming, realistic time histories is precisely the tool that performance-based earthquake engineering workflows need for large ensembles of nonlinear structural analyses.

The practical implications extend across seismic hazard analysis, code-based design verification, and the growing field of physics-informed machine learning for earthquake engineering. Fragility curves, which estimate the probability of structural damage as a function of shaking intensity, depend on suites of ground motions that are both numerous and spectrally appropriate; generative sampling offers a way to build such suites without the record-scaling artifacts that contaminate many existing studies. The authors note that the codes developed in the work will be made publicly available on GitHub, and that the underlying records come from the openly accessible NGA-West2 database, lowering the barrier for other research groups to adopt and stress-test the approach. Funded by the National Natural Science Foundation of China under grants 52192675 and 52378541, the work signals a broader shift in which the stochastic simulation of earthquake shaking, once the province of hand-tuned spectral matching algorithms, is increasingly delegated to neural networks that learn the statistics of real ground motion directly from data, and then generate exactly the shaking scenarios engineers ask for.

Subject of Research: Generative AI-based simulation of spectrum-compatible earthquake ground motion time histories

Article Title: Generative AI for spectrum-compatible ground motion time history simulation

Article References: Hu, X., Chen, S., Ding, Y., Liu, X., Fu, L., Zhao, Q., & Li, X. (2026). Generative AI for spectrum-compatible ground motion time history simulation. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-026-02699-z

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02699-z

Keywords: generative artificial intelligence, STFT-VAEGAN, ground motion simulation, response spectrum matching, earthquake engineering, variational autoencoder, generative adversarial network, Wasserstein GAN, NGA-West2 database, ground motion model, nonstationary waveforms, seismic hazard analysis