Estimation of Site Amplification from Geotechnical Array Data Using Neural Networks

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.