Investigating the Applicability of Integrated Hydrological Modeling for Mapping Regional Liquefaction Hazard
Soil liquefaction and related phenomena, such as ground deformation and lateral spreading, pose significant risk to distributed and critical infrastructure systems. Although liquefaction vulnerability is controlled by geologic and groundwater conditions, its regional assessment is almost exclusively based on geologic material properties or proxies thereof. In this work, we are developing a multivariate methodology that incorporates groundwater conditions by introducing hydrological variables in regional assessment of liquefaction hazards. More specifically, we use remote sensing data and well-monitoring data to set up an integrated hydrological model that provides estimates of soil moisture and depth to water table. We demonstrate the methodology by presenting a case study from the 2010 El Mayor-Cucapah earthquake in Imperial County. We use reconnaissance data to train a logistic regression model, which yields probabilistic maps of liquefaction occurrence. Preliminary results indicate that the proposed approach may improve the modeling of regional liquefaction assessment at no additional site investigation cost. However, in order to draw quantitative conclusions on the accuracy improvements, more training data is necessary for which we are collaborating with the ARIA Center at JPL-Caltech.