With the recent rise of neural operators, scientific machine learning offers new solutions to quantify uncertainties associated with high-fidelity numerical simulations. Traditional neural networks, such as Convolutional Neural Networks (CNN) or Physics-Informed Neural Networks (PINN), are restricted to the prediction of solutions in a predefined configuration. With neural operators, one can learn the general solution of Partial Differential Equations, such as the elastic wave equation, with varying parameters. There have been very few applications of neural operators in seismology. All of them were limited to two-dimensional settings, although the importance of three-dimensional (3D) effects is well known. In this work, we apply the Fourier Neural Operator (FNO) to predict ground motion time series from a 3D geological description. We used a high-fidelity simulation code, SEM3D, to build an extensive database of ground motions generated by 30,000 different geologies. With this database, we show that the FNO can produce accurate ground motion even when the underlying geology exhibits large heterogeneities. Intensity measures at moderate and large periods are especially well reproduced. We present the first seismological application of Fourier Neural Operators in 3D. Thanks to the generalizability of our database, we believe that our model can be used to assess the influence of geological features such as sedimentary basins on ground motion, which is paramount to evaluating site effects.
翻译:随着神经算子的出现,科学机器学习为高保真数值模拟的不确定性提供了新的解决方案。传统的神经网络,如卷积神经网络 (CNN) 或面向物理的神经网络 (PINN),仅限于预测预定义配置的解决方案。通过神经算子,可以学习偏微分方程 (如弹性波方程) 的一般解,且能处理不同的参数。虽然三维地震效应的重要性众所周知,但神经算子在地震学中的应用非常少。所有这些应用都限于二维设置。在这项工作中,我们将傅立叶神经算子(FNO)应用于预测三维地质描述中的地面运动时间序列。我们使用高保真度数值模拟代码SEM3D,建立了包含30,000多种地质特征地面运动的数据库。通过这个数据库,我们证明了FNO即使在底层地质具有大的异质性时也能产生准确的地面运动。特别是在中等和大周期下,强度测量值得到了很好的复制。我们展示了傅立叶神经算子在三维地震学中的首次应用。由于数据库的通用性,我们相信我们的模型可以用于评估沉积盆地等地质特征对地面运动的影响,这对于评估场地效应至关重要。