Nuclear reactor buildings must be designed to withstand the dynamic load induced by strong ground motion earthquakes. For this reason, their structural behavior must be assessed in multiple realistic ground shaking scenarios (e.g., the Maximum Credible Earthquake). However, earthquake catalogs and recorded seismograms may not always be available in the region of interest. Therefore, synthetic earthquake ground motion is progressively being employed, although with some due precautions: earthquake physics is sometimes not well enough understood to be accurately reproduced with numerical tools, and the underlying epistemic uncertainties lead to prohibitive computational costs related to model calibration. In this study, we propose an AI physics-based approach to generate synthetic ground motion, based on the combination of a neural operator that approximates the elastodynamics Green's operator in arbitrary source-geology setups, enhanced by a denoising diffusion probabilistic model. The diffusion model is trained to correct the ground motion time series generated by the neural operator. Our results show that such an approach promisingly enhances the realism of the generated synthetic seismograms, with frequency biases and Goodness-Of-Fit (GOF) scores being improved by the diffusion model. This indicates that the latter is capable to mitigate the mid-frequency spectral falloff observed in the time series generated by the neural operator. Our method showcases fast and cheap inference in different site and source conditions.

翻译：核反应堆建筑必须设计得能够承受强地震动引发的动态荷载。因此，必须在多种真实的地震动场景（例如最大可信地震）下评估其结构行为。然而，在目标区域可能并不总能获得地震目录和记录的地震图。因此，合成地震动正逐步得到应用，尽管需要采取一些必要的预防措施：地震物理学有时未能被充分理解，难以用数值工具精确复现，且其背后的认知不确定性会导致模型校准相关的计算成本过高。本研究提出一种基于人工智能物理原理的方法来生成合成地震动，该方法结合了一个在任意震源-地质构造中近似弹性动力学格林算子的神经算子，并通过去噪扩散概率模型进行增强。扩散模型被训练用于校正由神经算子生成的地震动时间序列。我们的结果表明，这种方法有望提升所生成合成地震图的真实性，扩散模型改善了频率偏差和拟合优度得分。这表明扩散模型能够缓解在神经算子生成的时间序列中观察到的中频频谱衰减。我们的方法在不同场地和震源条件下展示了快速且低成本推理的优势。