A latent Gaussian process model for the spatial distribution of liquefaction manifestation. Issue 2 (May 2023)

• Record Type: Journal Article
• Authors: Bullock, Zach; Zimmaro, Paolo; Lavrentiadis, Grigorios; Wang, Pengfei; Ojomo, Olaide; Asimaki, Domniki; Rathje, Ellen M; Stewart, Jonathan P
• Is Part Of: Earthquake spectra. Volume 39:Issue 2(2023)
• Page Start: 1189
• Page End: 1213
• Publication Date: 2023-05
• Subjects: Liquefaction -- Gaussian process -- geospatial proxy -- random fields -- spatial distribution -- regional manifestation; Earthquake engineering -- Periodicals; Earthquake engineering; Periodicals; 624.1762
• DOI: 10.1177/87552930231163894
• Languages: English
• ISSNs: 8755-2930

Abstract

This paper presents a model for distributing zones of liquefaction and nonliquefaction for use in regional liquefaction risk analysis. There are two broad methodologies that have been used to evaluate liquefaction risk on the regional scale: (a) application of site-specific procedures using soil properties inferred from geology, or (b) application of geospatial proxies for liquefaction. The first approach will tend to predict similar liquefaction probabilities across broad areas with similar geology, water table depths, and shaking intensities. The second approach yields the probability of liquefaction, which can be interpreted as the portion of the area affected by liquefaction (% A liq ). Neither approach, however, gives an informed prediction of the spatial distribution of liquefaction and the resulting displacements, which are particularly important for assessments of seismic risk for spatially distributed infrastructure systems. We propose a methodology for incorporating spatial correlation into a geospatial proxy for liquefaction to create maps of liquefaction and nonliquefaction for a given earthquake scenario. First, we describe a latent Gaussian process that is assumed to govern the spatial distribution of liquefaction. Next, a database of empirical observations of liquefaction is used to obtain the coefficients that describe that latent Gaussian process. The proposed model yields random realizations of maps of liquefaction and nonliquefaction conditioned on a map of% A liq . Such maps can be used to constrain the area over which displacements are estimated using soil properties inferred from geology and are therefore a critical component in reducing bias in assessments of liquefaction risk at the regional scale.