Abstract

Land-use planning in regard of earthquake-triggered landslides is usually implemented by means of the production of hazard maps. The well-known Newmark rigid block methodology is the most frequent used approach for this purpose. In this method, slope stability is evaluated by the estimation of the Newmark displacement, which is used to set different categories of hazard. This methodology presents limitations due to the difficulty of incorporating the variability of the used variables. For that reason, the logic-tree approach has been used in order to incorporate the epistemic uncertainties and compute probabilistic seismic-landslide hazard maps. However, the used weights in the logic-tree are usually set for each branch based on an expert judgement or subjective criteria. This article provide data obtained from the use of logic-tree methodology; this dataset is useful for deriving the unbiased weights to use in such methodology and in moderate-to-low magnitude scenarios. The data presented here are related to the article entitled “Obtaining suitable logic-tree weights for probabilistic earthquake-induced landslide hazard analyses” (Rodríguez-Peces et al., 2020) [1].

Highlights

  • Land-use planning in regard of earthquake-triggered landslides is usually implemented by means of the production of hazard maps

  • Earth and Planetary Sciences Engineering Geology, Seismically-induced landslides Table (Excel file) Following a logic-tree schema, a set of values representative of variables that participate in the evaluation of Newmark displacement were chosen

  • Dataset comprises several columns depicting the value of input parameters and additional columns showing%GFC and%TAC

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Summary

Introduction

Land-use planning in regard of earthquake-triggered landslides is usually implemented by means of the production of hazard maps. Unbiased logic-tree data for earthquake-induced landslide hazard maps for low-to-moderate magnitude events The logic-tree approach has been used in order to incorporate the epistemic uncertainties and compute probabilistic seismic-landslide hazard maps.

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