Spatial correlation of systematic effects of non-ergodic ground motion models in the Ridgecrest area

Liu, Chenying; Macedo, Jorge; Kuehn, Nicolas

doi:10.1007/s10518-022-01441-9

Cited by 4 publications

(4 citation statements)

References 55 publications

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“…Bayesian inference provides a method to solve the uncertainty of the model by using probability theory, which can model the uncertainty of the non-ergodic GMM as probability distribution (Liu et al, 2022a(Liu et al, , 2022bMacedo and Liu, 2022). The integrated nested Laplace approximation (INLA) method can be implemented using the R-INLA package (www.r-inla.org) in the R computing environment (Lindgren and Rue, 2015;Rue et al, 2017).…”

Section: Bayesian Inference With the Integrated Nested Laplace Approx...mentioning

confidence: 99%

“…In addition, several recent studies (e.g. Abrahamson et al, 2019;Kuehn, 2021;Kuehn et al, 2019;Landwehr et al, 2016;Liu et al, 2022aLiu et al, , 2022bMacedo and Liu, 2022;Sung et al, 2021) have shown regional differences in the scaling of ground motion represented by the non-ergodic GMMs, revealing systematic and repeatable source, path, and site effects. These studies indicate that the non-ergodic model can capture epistemic uncertainty of spatially varying effects.…”

Section: Introductionmentioning

confidence: 99%

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An offshore non-ergodic ground motion model for subduction earthquakes in Japan Trench area

Hu,

Li,

2023

Earthquake Spectra

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With the improvement of the world’s largest seafloor observation network (S-net) and the increase in the quantity and quality of records, the ergodic assumptions can be further relaxed in the modeling of offshore ground motion models (GMMs). This allows accounting for systematic and repeatable source, site, and path effects to further understand the characteristics of offshore ground motion in the Japan Trench region. We developed an offshore ergodic backbone GMM for subduction earthquakes and classified the sites into four categories using horizontal–vertical response spectral ratio to investigate site amplification. The offshore ergodic GMM is applicable for subduction earthquake scenarios with moment magnitudes ranging from 4.0 to 7.4 and rupture distances ranging from 10 to 300 km. Comparing offshore ergodic GMMs with onshore GMMs for subduction earthquakes, we found that offshore GMMs were significantly different from onshore GMMs, especially in the long-period and unburied states. Then a new offshore non-ergodic GMM was developed based on the offshore ergodic GMM. The systematic and repeatable source and site effects were captured by the spatially varying coefficients represented by Gaussian processes, while the systematic and repeatable path effects were captured by cell-specific anelastic attenuation proposed by Dawood and Rodriguez-Marek (2013), calculated with the Cohen-Sutherland computer graphics algorithm. The non-ergodic GMM revealed systematic and repeatable source, site, and path effects that were not captured by the ergodic GMM. Moreover, the non-ergodic GMM showed reduced aleatory variability and epistemic uncertainty on ground motion estimation compared to ergodic GMM. The reduction of aleatory variability and epistemic uncertainty had a significant impact on probabilistic seismic hazard analysis. Quantifications of these results are contributed to conduct reasonable seismic design and seismic risk assessment for marine engineering in offshore regions vulnerable to strong subduction earthquakes.

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Section: Bayesian Inference With the Integrated Nested Laplace Approx...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An offshore non-ergodic ground motion model for subduction earthquakes in Japan Trench area

Hu,

Li,

2023

Earthquake Spectra

View full text Add to dashboard Cite

show abstract

“…While these functions are typically defined for Euclidean distance and have a similar functional form as Eq. (3), Kuehn and Abrahamson (2020) and Liu et al (2022b) recently proposed varying the Euclidean length scale, E , as a function of the epicentral distance. This also induces path effects that vary spatially in a more complex manner than only Euclidean distance.…”

Section: Accounting For Path Effects Using An Angular Distance Metricmentioning

confidence: 99%

“…We chose weakly informative prior distributions for the parameters based on guidance provided in Kuehn and Stafford (2021) and Liu et al (2022b) and assume that their joint distribution, p(ψ M ) in Eq. ( 8), is factorizing.…”

Section: Prior Distributionsmentioning

confidence: 99%

Accounting for path and site effects in spatial ground-motion correlation models using Bayesian inference

Bodenmann¹,

Baker²,

Stojadinović³

2023

Nat. Hazards Earth Syst. Sci.

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Abstract. Ground-motion correlation models play a crucial role in regional seismic risk modeling of spatially distributed built infrastructure. Such models predict the correlation between ground-motion amplitudes at pairs of sites, typically as a function of their spatial proximity. Data from physics-based simulators and event-to-event variability in empirically derived model parameters suggest that spatial correlation is additionally affected by path and site effects. Yet, identifying these effects has been difficult due to scarce data and a lack of modeling and assessment approaches to consider more complex correlation predictions. To address this gap, we propose a novel correlation model that accounts for path and site effects via a modified functional form. To quantify the estimation uncertainty, we perform Bayesian inference for model parameter estimation. The derived model outperforms traditional isotropic models in terms of the predictive accuracy for training and testing data sets. We show that the previously found event-to-event variability in model parameters may be explained by the lack of accounting for path and site effects. Finally, we examine implications of the newly proposed model for regional seismic risk simulations.

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Spatial correlation analysis for ANN generated physics-based broadband response spectra: A case study for 2023 Turkey events

Sharma,

Dhanya,

Gade

et al. 2024

J Earth Syst Sci

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Spatial correlation of systematic effects of non-ergodic ground motion models in the Ridgecrest area

Cited by 4 publications

References 55 publications

An offshore non-ergodic ground motion model for subduction earthquakes in Japan Trench area

An offshore non-ergodic ground motion model for subduction earthquakes in Japan Trench area

Accounting for path and site effects in spatial ground-motion correlation models using Bayesian inference

Spatial correlation analysis for ANN generated physics-based broadband response spectra: A case study for 2023 Turkey events

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