GNGTS 2015 - Atti del 34° Convegno Nazionale

Contribution of the cluster analysis of HVSR data for near surface geological reconstruction P. Capizzi 1 , R. Martorana 1 , A. D’Alessandro 2 , D. Luzio 1 1 Dip. Scienze della Terra e del Mare, Università di Palermo, Italy 2 Istituto Nazionale di Geofisica e Vulcanologia, Centro Nazionale Terremoti, Italy Introduction. 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. Amodified 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). The cluster analysis. The cluster analysis is the procedure that allows to identify within a set of objects some subsets, called clusters, that tend to be homogeneous within them, according to some criteria. The statistical units are divided into a number of groups according to their level of similarity (internal cohesion), evaluated from the values ​that a number of variables chosen takes in each unit. Generally, in the analysis for grouping is not necessary to have in mind any interpretative model (Fabbris, 1983). The partition is successful if the objects within the clusters are closer to each other than other in different clusters (Barbarito, 1999). Many clustering algorithms exist (Gan et al. , 2007; Everit et al. , 2011), and can be categorized into two main types: Hierarchical Clustering (HC) and Non-Hierarchical Clustering (NHC). The HC have numerous advantages compared to the NHC. The HC are explorative methods and is not necessary to define a priori the number of clusters. The HC work with a measure of proximity between the objects to be grouped together. A type of proximity can be chosen which is suited to the subject studied and the nature of the data. One of the results of HC is the dendrogram which shows the progressive grouping of the data. It is then easy possible to gain an idea of a suitable number of classes into which the data can be grouped. To evaluate the differences between the various clustering techniques, which can also produce results significantly different from each other, the best way is to assess how the different techniques reproduce the structure of known data. These assessments are typically performed on simulated data, and are often difficult to interpret and may be contradictory. The elements that seem to influence more the results of this analysis are: 50 GNGTS 2015 S essione 3.2