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The effects induced by the choice of numerical base conditions for evaluating local seismic response are investigated in this technical note, aiming to provide guidelines for professional applications. A numerical modelling of the seismic site response is presented, assuming a one-dimensional scheme. At first, with reference to the case of a homogeneous soil layer overlying a half-space, two different types of numerical base conditions, named rigid and elastic, were adopted to analyse the seismic site response. Then, geological setting, physical and mechanical properties were selected from Italian case studies. In detail, the following stratigraphic successions were considered: shallow layer 1 (shear wave velocity, VS, equal to 400 m/s), layer 2 (VS equal to 600 m/s) and layer 3 (VS equal to 800 m/s). In addition, real signals were retrieved from the web site of the Italian accelerometric strong motion network. Rigid and elastic base conditions were adopted to estimate the ground motion modifications of the reference signals. The results are presented in terms of amplification factors (i.e., ratio of integral quantities referred to free-field and reference response spectra) and are compared between the adopted numerical models.

Abstract. Past seismic events worldwide demonstrated that damage and death toll depend on both the strong ground motion (i.e., source effects) and the local site effects. The variability of earthquake ground motion distribution is caused by the local stratigraphic and/or topographic setting and buried morphologies (e.g., irregular sub-interface between soft and stiff soils) that can give rise to amplification and resonances with respect to the ground motion expected at the reference site. Therefore, local site conditions can affect an area with damage related to the full collapse or loss in functionality of facilities, roads, pipelines, and other lifelines. To this concern, the near-real-time prediction of ground motion variation over large areas is a crucial issue to support the rescue and operational interventions. A machine learning approach was adopted to produce ground motion prediction maps considering both stratigraphic and morphological conditions. A set of about 16 000 accelerometric data points and about 46 000 geological and geophysical data points was retrieved from Italian and European databases. The intensity measures of interest were estimated based on nine input proxies. The adopted machine learning regression model (i.e., Gaussian process regression) allows for improving both the precision and the accuracy in the estimation of the intensity measures with respect to the available near-real-time prediction methods (i.e., ground motion prediction equation and ShakeMaps). In addition, maps with a 50 m × 50 m resolution were generated, providing a ground motion variability in agreement with the results of advanced numerical simulations based on detailed subsoil models.