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. Past seismic events worldwide demonstrated that damage and deathtoll depend on both the strong ground motion (i.e., source effects) and thelocal site effects. The variability of earthquake ground motion distributionis caused by the local stratigraphic and/or topographic setting and buriedmorphologies (e.g., irregular sub-interface between soft and stiff soils)that can give rise to amplification and resonances with respect to theground motion expected at the reference site. Therefore, local siteconditions can affect an area with damage related to the full collapse orloss in functionality of facilities, roads, pipelines, and other lifelines.To this concern, the near-real-time prediction of ground motion variation over large areasis a crucial issue to support the rescue and operational interventions. Amachine learning approach was adopted to produce ground motion predictionmaps considering both stratigraphic and morphological conditions. A set ofabout 16 000 accelerometric data points and about 46 000 geological and geophysicaldata points was retrieved from Italian and European databases. The intensitymeasures of interest were estimated based on nine input proxies. The adoptedmachine learning regression model (i.e., Gaussian process regression) allowsfor improving both the precision and the accuracy in the estimation of theintensity 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 motionvariability in agreement with the results of advanced numerical simulationsbased on detailed subsoil models.

Ground motion prediction maps using seismic-microzonation data and machine learning