Probabilistic regional-scale liquefaction triggering modeling using 3D Gaussian processes. Issue 134 (July 2020)

• Record Type: Journal Article
• Authors: Greenfield, Michael W.; Grant, Alex
• Is Part Of: Soil dynamics and earthquake engineering. Issue 134(2020)
• Publication Date: 2020-07
• Subjects: Liquefaction susceptibility -- Probabilistic liquefaction triggering -- Regional hazard analysis -- Spatial interpolation -- Gaussian process model -- Kriging -- Cascadia subduction zone; Soil dynamics -- Periodicals; Earthquake engineering -- Periodicals; Sols -- Dynamique -- Périodiques; Génie parasismique -- Périodiques; 624.176205
• DOI: 10.1016/j.soildyn.2020.106159
• Languages: English
• ISSNs: 0267-7261

Abstract

Abstract: Liquefaction is a major cause of coseismic damages, occurring irregularly over hundreds or thousands of square kilometers in large earthquakes. Large variations in the extent and location of liquefaction have been observed in recent earthquakes, motivating the need for prediction methods that consider the spatial heterogeneity of geologic deposits at a regional scale. Contemporary regional-scale liquefaction hazard analyses are typically performed using only surficial data, which does not address the complicated subsurface mechanics and spatial variability associated with artificial fill and natural soil deposits. In this study, we develop a probabilistic, regional-scale, subsurface model using data from hundreds of borings to better understand subsurface conditions that could influence liquefaction. We then use this subsurface sample database to train Gaussian process models, yielding 3D independent random fields of groundwater depth, soil plasticity, and penetration resistance for each geologic unit. We incorporate the Gaussian process models into probabilistic liquefaction triggering procedures, producing 3D estimates of the probability of liquefaction for an example study area in Portland, Oregon. Near sampling locations, the variance of the Gaussian process models approaches the variance of site-specific liquefaction triggering procedures. Conversely, when no sample data are nearby to condition a Gaussian process, the variance approaches the marginal variance of the entire recorded dataset. Thus, the procedure described in this study unifies probabilistic site-specific and regional-scale liquefaction triggering procedures and provides an important step towards quantitative liquefaction hazard assessments for regionally distributed infrastructures, such as levees, pipelines, roadways, and electrical transmission facilities. Highlights: Gaussian processes models consider multiple sources of spatially heterogeneous subsurface data for liquefaction analyses. Statistical models unify probabilistic site-specific and regional-scale liquefaction triggering procedures. Our regional-scale subsurface model accurately matches site-specific estimates with about 80% accuracy over a 460 km 2 area.