Suchergebnisse

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.