Correspondence between Site Amplification and Topographical, Geological Parameters: Collation of Data from Swiss and Japanese Stations, and Neural Networks-Based Prediction of Local Response

Abstract

We address the relation between seismic local amplification and topographical and geological indicators describing the site morphology. We focus on parameters that can be derived from layers of diffuse information (e.g., digital elevation models, geological maps) and do not require in situ surveys; we term these parameters as “indirect” proxies, as opposed to “direct” indicators (e.g., f0, VS30 ) derived from field measurements. We first compiled an extensive database of indirect parameters covering 142 and 637 instrumented sites in Switzerland and Japan, respectively; we collected topographical indicators at various spatial extents and focused on shared features in the geological descriptions of the two countries. We paired this proxy database with a companion dataset of site amplification factors at 10 frequencies within 0.5–20 Hz, empirically measured at the same Swiss and Japanese stations. We then assessed the robustness of the correlation between individual site-condition indicators and local response by means of statistical analyses; we also compared the proxy-site amplification relations at Swiss versus Japanese sites. Finally, we tested the prediction of site amplification by feeding ensembles of indirect parameters to a neural network (NN) structure. The main results are: (1) indirect indicators show higher correlation with site amplification in the low-frequency range (0.5–3.33 Hz); (2) topographical parameters primarily relate to local response not because of topographical amplification effects but because topographical features correspond to the properties of the subsurface, hence to stratigraphic amplification; (3) large-scale topographical indicators relate to low-frequency response, smaller-scale to higher-frequency response; (4) site amplification versus indirect proxy relations show a more marked regional variability when compared with direct indicators; and (5) the NN-based prediction of site response is the best achieved in the 1.67–5 Hz band, with both geological and topographical proxies provided as input; topographical indicators alone perform better than geological parameters.