A new Vs30 map for Italy based on the seismic microzonation dataset

In this study, we derive a large-scale Vs30 map for Italy starting from the global terrain geomorphological classification based on normalized slope, local convexity, and surface texture (after Iwahashi et al., 2018). The novelty of the present work is the integration of a large amount of data from the Italian seismic microzonation dataset, consisting in about 35,000 borehole logs and 11,300 Vs profiles. The results of our analysis suggest that the geomorphological classes are correlated to subsoil lithologies and Vs30 values more than surface lithological types (after Amanti et al., 2008). As already done in Japan (Matsuoka and Wakamatsu, 2006), Greece (Stewart et al., 2014), Central and Eastern North America (Parker et al., 2017), and Pacific Northwest Region of North America (Ahdi et al., 2017), we found a proxy dependence of Vs30 from slope gradient and elevation through a lognormal linear regression model for each geomorphological class. A stepwise method allowed us to give meaning and statistical robustness to the regression model dependencies. The polygons inconsistent with the Iwahashi et al. (2018) approach (e.g., Meso-Cenozoic carbonate units of Apulian block and Hyblean Plateau related to the foreland domain) were replaced with the polygons of the lithological map (Amanti et al., 2008) and the associated Vs30 lognormal average values. Therefore, a hybrid raster Vs30 map was created with a spatial resolution of 50×50 m. The comparison with other previous maps shows how the forecast improves in terms of both accuracy and precision. For these reasons, the new Vs30 proxy-based map for Italy based on the seismic microzonation dataset represents a useful tool for improving several applications: ShakeMaps, stochastic event based hazard map, total hazard map, assessment of coseismic instabilities, seismic risk assessment for spatially distributed emergency and resilience systems. Vs30 raster maps and supplementary material are available in the Research Data (doi:10.17632/8458tgzc73.1).