New Frontiers in Seismic Hazard and Risk Analyses of Infrastructure Systems

Grigorios Lavrentiadis, PhD

Postdoctoral research associate, Mechanical and Civil Engineering Department, California Institute of Technology

February 9, 2024 | 11 a.m. | 140 Ketter Hall


grigorios lavrentiadis.

Infrastructure systems serve as the backbone of a nation’s growth, yet their exposure to natural hazards places economic activities and development in peril. In this context, we explore recent advancements in fault displacement and ground motion modeling to better assess the earthquake hazard. Fault Displacement and Ground Motion Models are key components in probabilistic fault displacement and seismic hazard analyses (PFDHA) and (PSHA), providing the range of expected tectonic displacements and ground-motion shaking for all considered scenarios.

A suite of new Fault-Displacement Models (FDMs) are developed for the aggregate, principal, and distributed net surface displacement based on the Fault Displacement Hazard Initiative database. Key objectives of this effort were constraining the extrapolation to rare large events, capturing the magnitude dependence of the along-strike profiles, and improving the modeling of aleatory variability. These goals are achieved through the incorporation of seismological constraints, explicit modeling of segmentation, and partition of displacements to overlapping ruptures. The use of a power-normal distribution leads to a narrower range of displacements for large-magnitude events compared to traditional models, significantly reducing the hazard for critical infrastructure sensitive to rare events. Comparing the expected range of maximum displacements with observations supports the choice of seismology-based scaling and the narrower shape of the upper tail of the power-normal distribution. Efforts to relax the ergodic assumption in Ground-Motion Models (GMMs) have led to the biggest changes in seismic hazard in recent years. Traditional GMMs are based on the ergodic assumption, assuming the variability of ground motion across space is consistent with the variability of ground motion at a single location, leading to inflated aleatory variability. Through a Gaussian Process regression, advancement in computational efficiency, and development of physics-inspired kernel functions, we proposed a new series of non-ergodic ground motion models that aim to address the simplified assumptions in traditional models and properly capture the spatially varying repeatable source, path, and site effects. These new NGMMs result in a more accurate estimation of the site-specific seismic hazard, often leading to significantly different hazard curves, especially at large return periods that are of interest for critical infrastructure.


Grigorios (Greg) Lavrentiadis is a postdoctoral research associate at the Mechanical and Civil Engineering Department at the California Institute of Technology. His current research focuses on developing machine-learning methodologies for ground motion synthesis, post-disaster reconnaissance, and the development of physics-informed non-ergodic ground motion models (NGMMs). He received his Ph.D. in Civil and Environmental Engineering from the University of California, Berkeley, in 2021. Before his PhD, he worked for two years at Fugro USA as part of the Global Services - Earthquake Engineering group. He earned his bachelor's degree from Aristotle University of Thessaloniki, Greece in 2014 and his master's of science degree from the University of California, Berkeley, in 2015.