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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 and a convolutional neural network (NN) using observed mean AFs observed at ∼ 600 KiK-net and California Strong Motion Instrument Program (CSMIP) vertical array sites. Compared to predictions based on theoretical SH 1D amplifications, the 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.