Predictive model of Arias intensity and Newmark displacement for regional scale evaluation of earthquake-induced landslide hazard in Greece

摘要

Defining the possible scenario of earthquake-induced landslides, Arias intensity is frequently used as a shaking parameter, being considered the most suitable for characterising earthquake impact, while Newmark's sliding-block model is widely used to predict the performance of natural slopes during earthquake shaking. In the present study we aim at providing tools for the assessment of the hazard related to earthquake-induced landslides at regional scale, by means of new empirical equations for the prediction of Arias intensity along with an empirical estimator of coseismic landslide displacements based on Newmark's model. The regression data, consisting of 205 strong motion recordings relative to 98 earthquakes, were subdivided into a training dataset, used to calculate equation parameters, and a validation dataset, used to compare the prediction performance among different possible functional forms and with equations derived from previous studies carried out for other regions using global and/or regional datasets. Equations predicting Arias intensities expected in Greece at known distances from seismic sources of defined magnitude proved to provide more accurate estimates if site condition and focal mechanism influence can be taken into account. Concerning the empirical estimator of Newmark displacements, we conducted rigorous Newmark analysis on 267 one-component records yielding a dataset containing 507 Newmark displacements, with the aim of developing a regression equation that is more suitable and effective for the seismotectonic environment of Greece and could be used for regional-scale seismic landslide hazard mapping. The regression analysis showed a noticeable higher goodness of fit of the proposed relations compared to formulas derived from worldwide data, suggesting a significant improvement of the empirical relation effectiveness from the use of a regionally-specific strong-motion dataset (C) 2014 Elsevier Ltd. All rights reserved.