Contribution of the cluster analysis of HVSR data for near surface geological reconstruction

Abstract

The use of HVSR technique allows in many cases (Bonnefoy-Claudet et al. 2006) to obtain detailed reconstruction of the roof of the seismic bedrock (Di Stefano et al. 2014) and to identify areas with similar seismic behaviour. Theoretical considerations (Nakamura 1989) and experimental tests showed that amplification of horizontal motions between bottom and top of a sedimentary cover is well related to the ratio between the spectra of the horizontal and vertical components of the ground velocity (Nakamura 2000). This ratio is a measure of ellipticity of Rayleigh wave polarization, overlooking Love and body waves contribution. Assuming that subsoil can be represented as a stack of homogeneous horizontal layers and imposing some geometric and/or physical constraints it is possible to estimate the parameters of the shear wave velocity model (Fäh et al. 2003; Parolai et al. 2000). The integration of data related to HVSR and active techniques based on the analysis of surface waves can greatly reduce the uncertainties on the interpretation models. Because the inversion of HVSR curves implies monodimensional distribution of Vs, before inversing the data we used a cluster analysis technique to subdivide them into subsets attributable to areas with low horizontal velocity gradients and therefore similar seismic responses. The data of each cluster were then interpreted by imposing conditions of maximum similarity between the 1D models relating to each measurement point. Clustering methods are widely used in different research fields (Hartigan, 1975, Adelfio et al., 2012; D’Alessandro et al., 2013). In general, the cluster analysis is a good tool whenever you have to classify a large amount of information into meaningful and manageable groups. A modified centroid-based algorithm has been applied to HVSR datasets acquired for studies of seismic microzoning in various urban centers of Sicilian towns (Capizzi et al., 2014). The results obtained for Modica and Enna towns are shown. HVSR data were previously properly processed to extract frequency and amplitude of peaks by a code based on clustering of HVSR curves determined in sliding time windows (D’Alessandro et al. 2014).