We use deep learning to predict surface‐to‐borehole Fourier amplification functions (AFs) from discretized shear‐wave velocity profiles. Specifically, we train a fully connected neural network and a convolutional neural network using mean AFs observed at ∼600 KiK‐net vertical array sites. Compared with predictions based on theoretical SH 1D amplifications, the neural network (NN) results in up to 50% reduction of the mean squared log error between predictions and observations at sites not used for training. In the future, NNs may lead to a purely data‐driven prediction of site response that is independent of proxies or simplifying assumptions.

中文翻译：

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使用神经网络从岩土阵列数据估计场地放大

我们使用深度学习从离散的横波速度剖面预测地表到钻孔的傅立叶放大函数 (AF)。具体来说，我们使用在 600 个 KiK-net 垂直阵列站点上观察到的平均 AF 来训练一个完全连接的神经网络和一个卷积神经网络。与基于理论 SH 1D 放大的预测相比，神经网络 (NN) 可将未用于训练的站点的预测和观察之间的均方对数误差降低多达 50%。未来，神经网络可能会导致对站点响应的纯数据驱动预测，独立于代理或简化假设。