arXiv subjects

Grigorios Lavrentiadis

Publications and source records attributed to Grigorios Lavrentiadis.

9 recordsLinked to original sources

arXiv2026

Principal Surface Fault Rupture Hazard: Moving Beyond Aggregate Displacement Metrics

Structures crossing active faults are subjected to permanent ground deformation that can impose severe demands on infrastructure. Although probabilistic fault displacement hazard analysis (PFDHA) has advanced considerably, existing principal fault displacement models use aggregate displacement, implicitly assuming that overlapping rupture strands contribute simultaneously to site demand. This assumption is suitable for regional hazard characterization but does not represent finite structural dimensions, where only a subset of ruptures may intersect a structure. This study presents a probabilistic framework that reformulates aggregate principal displacement into a rupture-level representation for infrastructure-specific hazard assessment. Using the Fault Displacement Hazard Initiative (FDHI) database of 75 surface-rupturing earthquakes, principal rupture strands are reconstructed from mapped rupture geometries and displacement observations using event- and segment-based coordinate systems. The framework models three quantities: (1) the number of overlapping principal ruptures, (2) partitioning of aggregate displacement among individual rupture strands while preserving the aggregate displacement budget, and (3) the spatial distribution of rupture strands relative to the primary fault trace. These components are integrated into a generalized PFDHA formulation accounting for earthquake magnitude, style of faulting, rupture curvature, structural footprint, and fault-trace location uncertainty. Benchmark fault-crossing applications demonstrate the framework's ability to estimate the probability that individual rupture strands intersect a structure and the associated displacement demand. The framework provides a physically consistent extension of aggregate-based models and a basis for infrastructure-specific PFDHA that explicitly accounts for rupture geometry and finite crossing dimensions.

physics.geo-ph↗

arXiv2026

Scalable Gaussian Process for Learning Non-Ergodic Ground Motion Model from Physics-Based Simulations with Application to Power Infrastructure Assessment

This study presents the development and application of a scalable non-ergodic ground motion model (NGMM) for the Los Angeles area. The NGMM is trained and validated on physics-based simulated ground-motion data from a recent Statewide California Earthquake Center (SCEC) CyberShake study. The NGMM is formulated as a Gaussian Process (GP) regression model, where the prior median is defined as the ASK14 ergodic ground-motion model and the posterior median is obtained by learning the non-ergodic effects embedded in the training data. These non-ergodic effects include systematic site and path effects, which are represented in the GP using Matérn and specialized covariance kernels that explicitly characterize path vectors. Implementing the NGMM requires hyperparameter tuning and inference on large datasets (on the order of one million data points or more), posing significant computational challenges for conventional GP approaches. To address this scalability issue, this paper presents a suite of computational strategies, including sparse Cholesky inversion, parallel computing, GPU acceleration, and stochastic gradient descent minimization. Despite these advances, the full CyberShake dataset (on the order of hundreds of millions of data points) remains computationally prohibitive. Therefore, aleatory variability is modeled separately using a mixed-effects formulation to represent within-event and between-event variability. The developed NGMM has two primary applications: interpolation of partially observed ground-motion fields and predictive modeling for ground motions in unobserved earthquake scenarios. Validation results on independent datasets demonstrate accurate performance in both applications. A case study of power transmission network assessment in an Mw 6.7 Puente Hill scenario further demonstrated that the developed NGMM closely reproduces physics-based simulation results.

stat.AP↗

arXiv2026

Large-Scale 3D Ground-Motion Synthesis with Physics-Inspired Latent Operator Flow Matching

Earthquake hazard analysis and design of spatially distributed infrastructure, such as power grids and energy pipeline networks, require scenario-specific ground-motion time histories with realistic frequency content and spatiotemporal coherence. However, producing the large ensembles needed for uncertainty quantification with physics-based simulations is computationally intensive and impractical for engineering workflows. To address this challenge, we introduce Ground-Motion Flow (GMFlow), a physics-inspired latent operator flow matching framework that generates realistic, large-scale regional ground-motion time-histories conditioned on physical parameters. Validated on simulated earthquake scenarios in the San Francisco Bay Area, GMFlow generates spatially coherent ground motion across more than 9 million grid points in seconds, achieving a 10,000-fold speedup over the simulation workflow, which opens a path toward rapid and uncertainty-aware hazard assessment for distributed infrastructure. More broadly, GMFlow advances mesh-agnostic functional generative modeling and could potentially be extended to the synthesis of large-scale spatiotemporal physical fields in diverse scientific domains.

cs.LG↗

arXiv2024

Data-driven Characterization of Near-Surface Velocity in the San Francisco Bay Area: A Stationary and Spatially Varying Approach

This study presents the development of two new sedimentary velocity models for the San Francisco Bay Area (SFBA) to improve the near-surface representation of shear-wave velocity ($V_S$) for large-scale, broadband numerical simulations, with the ultimate goal of enhancing the representation of the sedimentary layers in the Bay Area community velocity model. The first velocity model is stationary and is based solely on $V_{S30}$; the second velocity model is spatially varying and has location-specific adjustments. They were developed using a dataset of 200 measured $V_S$ profiles. Both models were formulated within a hierarchical Bayesian framework, using a parameterization that ensures robust scaling. The spatially varying model includes a slope adjustment term modeled as a Gaussian process to capture site-specific effects based on location. Residual analysis shows that both models are unbiased for $V_S$ values up to 1000 m/sec. Along-depth variability models were also developed using within-profile residuals. The proposed models show higher $V_S$ in the San Jose area and Livermore Valley compared to the USGS Bay Area community velocity model by a factor of two or more in some cases. Goodness-of-fit (GOF) comparisons using one-dimensional linear site-response analysis at selected sites demonstrate that the proposed models outperform the USGS model in capturing near-surface amplification across a broad frequency range. Incorporating along-depth variability further improves the GOF scores by reducing over-amplification at high frequencies. These results underscore the importance of integrating data-driven models of the shallow crust, like the ones presented here, in coarser regional community velocity models to enhance regional seismic hazard assessments.

stat.AP↗

arXiv2024

Broadband Ground Motion Synthesis via Generative Adversarial Neural Operators: Development and Validation

We present a data-driven framework for ground-motion synthesis that generates three-component acceleration time histories conditioned on moment magnitude, rupture distance , time-average shear-wave velocity at the top $30m$ ($V_{S30}$), and style of faulting. We use a Generative Adversarial Neural Operator (GANO), a resolution invariant architecture that guarantees model training independent of the data sampling frequency. We first present the conditional ground-motion synthesis algorithm (cGM-GANO) and discuss its advantages compared to previous work. We next train cGM-GANO on simulated ground motions generated by the Southern California Earthquake Center Broadband Platform (BBP) and on recorded KiK-net data and show that the model can learn the overall magnitude, distance, and $V_{S30}$ scaling of effective amplitude spectra (EAS) ordinates and pseudo-spectral accelerations (PSA). Results specifically show that cGM-GANO produces consistent median scaling with the training data for the corresponding tectonic environments over a wide range of frequencies for scenarios with sufficient data coverage. For the BBP dataset, cGM-GANO cannot learn the ground motion scaling of the stochastic frequency components; for the KiK-net dataset, the largest misfit is observed at short distances and for soft soil conditions due to the scarcity of such data. Except for these conditions, the aleatory variability of EAS and PSA are captured reasonably well. Lastly, cGM-GANO produces similar median scaling to traditional GMMs for frequencies greater than 1Hz for both PSA and EAS but underestimates the aleatory variability of EAS. Discrepancies in the comparisons between the synthetic ground motions and GMMs are attributed to inconsistencies between the training dataset and the datasets used in GMM development. Our pilot study demonstrates GANO's potential for efficient synthesis of broad-band ground motions

physics.geo-ph↗

arXiv2022

A Seismologically Consistent Surface Rupture Length Model for Unbounded and Width-Limited Event

A new surface-rupture-length ($SRL$) relationship as a function of magnitude ($\mathbf{M}$), fault thickness, and fault dip angle is presented in this paper. The objective of this study is to model the change in scaling between unbounded and width-limited ruptures. This is achieved through the use of seismological-theory-based relationships for the average displacement scaling and the aid of dynamic fault rupture simulations to constrain the rupture width scaling. The empirical dataset used in the development of this relationship is composed of $123$ events ranging from $\mathbf{M}~5$ to $8.1$ and $SRL~1.1$ to $432~km$. The dynamic rupture simulations dataset includes $554$ events ranging from $\mathbf{M}~4.9$ to $8.2$ and $SRL~1$ to $655~km$. For the average displacement ($\bar{D}$) scaling, three simple models and two composite models were evaluated. The simple average displacement models were: a square root of the rupture area ($\sqrt{A}$), a down-dip width ($W$), and a rupture length ($L$) proportional model. The two composite models followed a $\sqrt{A}$ scaling for unbounded ruptures and transitioned to $W$ and $L$ scaling for width-limited events, respectively. The empirical data favors a $\bar{D} \sim \sqrt{A}$ scaling for both unbounded and width-limited ruptures. The proposed model exhibits better predictive performance compared to linear $\log(SLR)\sim\mathbf{M}$ type models, especially in the large magnitude range, which is dominated by width-limited events. A comparison with existing $SRL$ models shows consistent scaling at different magnitude ranges that is believed to be the result of the different magnitude ranges in the empirical dataset of the published relationships.

physics.geo-ph↗

arXiv2022

Overview and Introduction to Development of Non-Ergodic Earthquake Ground-Motion Models

This paper provides an overview and introduction to the development of non-ergodic ground-motion models, GMMs. It is intended for a reader who is familiar with the standard approach for developing ergodic GMMs. It starts with a brief summary of the development of ergodic GMMs and then describes different methods that are used in the development of non-ergodic GMMs with an emphasis on Gaussian Process (GP) regression, as that is currently the method preferred by most researchers contributing to this special issue. Non-ergodic modeling requires the definition of locations for the source and site characterizing the systematic source and site effects; the non-ergodic domain is divided into cells for describing the systematic path effects. Modeling the cell-specific anelastic attenuation as a GP and considerations on constraints for extrapolation of the non-ergodic GMMs are also discussed. An updated unifying notation for non-ergodic GMMs is also presented, which has been adopted by the authors of this issue.

stat.AP↗

arXiv2021

A Non-ergodic Spectral Acceleration Ground Motion Model for California Developed with Random Vibration Theory

A new approach for creating a non-ergodic $PSA$ ground-motion model (GMM) is presented which account for the magnitude dependence of the non-ergodic effects. In this approach, the average $PSA$ scaling is controlled by an ergodic $PSA$ GMM, and the non-ergodic effects are captured with non-ergodic $PSA$ factors, which are the adjustment that needs to be applied to an ergodic $PSA$ GMM to incorporate the non-ergodic effects. The non-ergodic $PSA$ factors are based on $EAS$ non-ergodic effects and are converted to $PSA$ through Random Vibration Theory (RVT). The advantage of this approach is that it better captures the non-ergodic source, path, and site effects through the small magnitude earthquakes. Due to the linear properties of Fourier Transform, the $EAS$ non-ergodic effects of the small events can be applied directly to the large magnitude events. This is not the case for $PSA$, as response spectrum is controlled by a range of frequencies, making $PSA$ non-ergodic effects depended on the spectral shape which is magnitude dependent. Two $PSA$ non-ergodic GMMs are derived using the ASK14 and CY14 GMMs as backbone models, respectively. The non-ergodic $EAS$ effects are estimated with the LAK21 GMM. The RVT calculations are performed with the V75 peak factor model, the $D_{a0.05-0.85}$ estimate of AS96 for the ground-motion duration, and BT15 oscillator-duration model. The California subset of the NGAWest2 database is used for both models. The total aleatory standard deviation of the two non-ergodic $PSA$ GMMs is approximately $30$ to $35\%$ smaller than the total aleatory standard deviation of the corresponding ergodic $PSA$ GMMs. This reduction has a significant impact on hazard calculations at large return periods. In remote areas, far from stations and past events, the reduction of aleatory variability is accompanied by an increase of epistemic uncertainty.

stat.AP↗

arXiv2021

A Non-ergodic Effective Amplitude Ground-Motion Model for California

A new non-ergodic ground-motion model (GMM) for effective amplitude spectral ($EAS$) values for California is presented in this study. $EAS$, which is defined in Goulet et al. (2018), is a smoothed rotation-independent Fourier amplitude spectrum of the two horizontal components of an acceleration time history. The main motivation for developing a non-ergodic $EAS$ GMM, rather than a spectral acceleration GMM, is that the scaling of $EAS$ does not depend on spectral shape, and therefore, the more frequent small magnitude events can be used in the estimation of the non-ergodic terms. The model is developed using the California subset of the NGAWest2 dataset Ancheta et al. (2013). The Bayless and Abrahamson (2019b) (BA18) ergodic $EAS$ GMM was used as backbone to constrain the average source, path, and site scaling. The non-ergodic GMM is formulated as a Bayesian hierarchical model: the non-ergodic source and site terms are modeled as spatially varying coefficients following the approach of Landwehr et al. (2016), and the non-ergodic path effects are captured by the cell-specific anelastic attenuation attenuation following the approach of Dawood and Rodriguez-Marek (2013). Close to stations and past events, the mean values of the non-ergodic terms deviate from zero to capture the systematic effects and their epistemic uncertainty is small. In areas with sparse data, the epistemic uncertainty of the non-ergodic terms is large, as the systematic effects cannot be determined. The non-ergodic total aleatory standard deviation is approximately $30$ to $40\%$ smaller than the total aleatory standard deviation of BA18. This reduction in the aleatory variability has a significant impact on hazard calculations at large return periods. The epistemic uncertainty of the ground motion predictions is small in areas close to stations and past events.

stat.AP↗