In this paper,we present an approach to generating probabilistic hazard maps for earthquake-induced landslides using the Newmark Displacement Model(NDM).This model takes the uncertainties associated with the slope pro...In this paper,we present an approach to generating probabilistic hazard maps for earthquake-induced landslides using the Newmark Displacement Model(NDM).This model takes the uncertainties associated with the slope properties(e.g.,soil shear strengths,groundwater table location)into consideration,which is coupled with the hydrological model based on geomorphological,geological,geotechnical,seismological,and rainfall data.Uncertainties and fluctuations in the input parameters of the NDM are considered by treating these quantities asβ-PERT distributions through Monte Carlo techniques,which allows probability value of the NDM to be cast into hazard maps.Additionally,incorporating Monte Carlo techniques can avoid using conservative input parameters in a deterministic approach to capture these uncertainties.Taking the 2017 Jiuzhaigou M_(w)6.5 Earthquake in Sichuan Province,Western China as an example,earthquake-induced landslides probability distribution map is generated with the most appropriate displacement threshold(λ=1 cm).Our results show good performances for realistic landslide hazard assessment,which can serve as a basis for providing a reference for the prediction of earthquake-induced landslide probability and rapid landslide hazard assessment after a strong earthquake.展开更多
文摘In this paper,we present an approach to generating probabilistic hazard maps for earthquake-induced landslides using the Newmark Displacement Model(NDM).This model takes the uncertainties associated with the slope properties(e.g.,soil shear strengths,groundwater table location)into consideration,which is coupled with the hydrological model based on geomorphological,geological,geotechnical,seismological,and rainfall data.Uncertainties and fluctuations in the input parameters of the NDM are considered by treating these quantities asβ-PERT distributions through Monte Carlo techniques,which allows probability value of the NDM to be cast into hazard maps.Additionally,incorporating Monte Carlo techniques can avoid using conservative input parameters in a deterministic approach to capture these uncertainties.Taking the 2017 Jiuzhaigou M_(w)6.5 Earthquake in Sichuan Province,Western China as an example,earthquake-induced landslides probability distribution map is generated with the most appropriate displacement threshold(λ=1 cm).Our results show good performances for realistic landslide hazard assessment,which can serve as a basis for providing a reference for the prediction of earthquake-induced landslide probability and rapid landslide hazard assessment after a strong earthquake.
