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.

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