GNGTS 2015 - Atti del 34° Convegno Nazionale

frequency ( f ), amplitude ( A ) and lithology ( L ): , where a, b, c and d are the weights. The choice of weights and of the optimal number of k classes have been optimized maximizing R parameter, taking into account the intra-cluster ( DEV IN ) and inter-cluster ( DEV OUT ) variances: . . However the use of a priori information especially on stratigraphic data is fundamental for the choice of number of partitions. Generally, experimental tests showed that different weights should be used to identify inclined or sub-horizontal seismic discontinuities. Application cases. During 2012, as part of an agreement with the Italian Department of Civil Protection, the expedite seismic microzonation has been performed in 20 municipalities of the Eastern Sicily considered at high seismic hazard. In addition to the collection of all previous geological and geophysical data, we have performed a passive seismic campaign to determine the resonance frequency of the investigated sites by means of the Nakamura technique (Nakamura, 1989). The HVSR cluster analyses were applied to HVSR data acquired in Modica and Enna towns. Multichannel Analysis of Surface Waves (MASW) were acquired in Enna town to constrain the interpretative models and the velocity of shear waves in the subsurface. HVSR data were inverted using similar starting models for each cluster. 1D seismic models were calculated using the code of Lunedei and Albarello (2009) based on the assumption that environmental noise is composed by the superimposition of random multi modal plane waves moving in all the directions at the surface of the Earth and propagating as Rayleigh and Love waves. Since body waves are not considered, this assumption is realistic only if sources are located far enough from the receiver. Consequently, all the time windows of the signal showing noise suspected to be caused by near sources must be removed. The HVSR clusters of peaks were considered to define the seismic layers, each characterized by a specific range of seismic velocities, and to associate them with the known geological formations. Inversion models of the different partitions obtained using the centroid-based algorithm were superimposed on the geological map of the analysed sites to identify possible correlations with geology and topography. In the case of Modica town the best cluster results seems to be a three partition, whereas in Enna site the cluster analysis converges to five groups (Fig. 2). In both case the map of the depth of the seismic bedrock (Fig. 3) and 3D model of seismic surfaces were reconstructed. Discussion. One of the typical criticisms to the cluster analysis is to arrive at indeterminate solutions, subject to arbitrary decisions relating to initial information, subjective interpretation of the results, and not statistically verifiable. Contrary to other statistical procedures, cluster analysis is often used when you do not have a priori hypotheses or when you are in the exploratory phase of analysis. However the application of cluster analysis, although falling between the methods of analysis essentially exploratory, should be preceded and accompanied by the definition of interpretative models. The time windows suitable for the determination of the mean HVSR are generally arbitrarily identified by the operator by a simple visual inspection of the microtremors signals in time or spectral domain. This can lead to an incorrect determination of the mean HVSR curves and to an incorrect interpretation of the main peaks. An automatic procedure, based on clustering analysis, for the determination of the appropriate windows to be used in the average HVSR curve determination has been implemented. This procedure allowed us to easy separate the HVSR curves and peak mainly linked to the site effects from those mainly related to the source effects. The analysis of the HVSR curves as a function of the azimuth result a useful tool for the characterization and discrimination of the major peaks identified on the HVSR curves. GNGTS 2015 S essione 3.2 53