Selection of earthquake ground motion models using the deviance information criterion

Kowsari, Milad; Halldórsson, Benedikt; Hrafnkelsson, Birgir; Jónsson, Sigurjón

doi:10.1016/j.soildyn.2018.11.014

Cited by 23 publications

(13 citation statements)

References 79 publications

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“…Thus, sigma of the GMM plays an important role in data-driven methods but it might be problematic either when performing non-ergodic or partially non-ergodic PSHA. Addressing these issues, (Kowsari et al 2019b) proposed a data-driven method using the Deviance Information Criterion (DIC) for selection of the most suitable GMM for application in PSHA. The main advantage of the DIC is to introduce the posterior sigma as a key unknown measure in order to rank the models objectively.…”

Section: Ranking Of Ground Motion Modelsmentioning

confidence: 99%

“…We also take advantage of the new Bayesian GMMs, establish the first backbone GMM for Iceland thus reducing the epistemic uncertainty in GMM predictions of Icelandic strong-motions and then investigate the effects on PSHA using the backbone approach vs. the classical approach for two key population centers in southwest Iceland i.e., one in the near-fault region (Selfoss) and the other in the farfield region (Reykjavik). Specifically, we apply a recently developed data-driven GMMranking method on several candidate GMMs developed from local, regional, and worldwide datasets (Kowsari et al 2019b). The data-driven ranking methods help to rank the GMMs in an objective way and thus reduces the epistemic uncertainty associated with the selected GMMs.…”

Section: Introductionmentioning

confidence: 99%

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A backbone seismic ground motion model for strike-slip earthquakes in Southwest Iceland and its implications for near- and far-field PSHA

Kowsari

Ghasemi

Bayat

et al. 2022

Bull Earthquake Eng

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The earthquake hazard and seismic risk in Iceland are the highest in the Southwest due to transform faulting in the South Iceland Seismic Zone (SISZ) and Reykjanes Peninsula Oblique Rift (RPOR). The reliable probabilistic seismic hazard assessment (PSHA) is therefore critical in this region which in turn requires two of the key elements for a reliable PSHA. Namely, the most appropriate and physically consistent ground motion models (GMMs) and the specification of seismic sources. In this study, we address this by employing a suite of new hybrid Bayesian empirical GMMs and a new physics-based finite-fault system model for the SISZ-RPOR system. By ranking the GMMs using the deviance information criterion against the Icelandic strong-motion dataset we propose backbone GMMs along with their Δ-factors that comprehensively capture the characteristics of Icelandic data. We then simulate suites of synthetic finite-fault earthquake catalogues that are consistent with the physics-based fault system. We then carry out a Monte-Carlo PSHA using the backbone approach for two representative near-fault and far-field sites in Southwest Iceland, but this approach provides a more realistic and robust treatment of the extent and effects of GMM's epistemic uncertainty on the PSHA. We show that the backbone PSHA is consistent with point estimates using the classical PSHA approach, but importantly they reveal the "body" i.e., the range, of realistically expected hazard values that dwarf any differences between the results from the two approaches. Thus, the backbone models of this study along with their Δ-factors are the ideal candidates for use in future PSHA for Iceland.

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Section: Ranking Of Ground Motion Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A backbone seismic ground motion model for strike-slip earthquakes in Southwest Iceland and its implications for near- and far-field PSHA

Kowsari

Ghasemi

Bayat

et al. 2022

Bull Earthquake Eng

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“…The second class consists of post hoc selection methods in which multiple models are fitted, and then compared using some scoring function to determine the optimal one. These methods include information criteria applied to the maximum a posteriori point such as the Bayesian and Akaike information criterion (Pachhai et al 2014;, information criterion applied to MCMC chains such as the deviance information criterion (Kowsari et al 2019), and cross-validation methods. This class also conceptually includes the class of highly underdetermined problems for which selection of damping and smoothing parameters via the l-curve, cross-validation or discrepancy principle methods (Aster et al 2018) or correction of the model towards a preferred form via the null-space shuttle (Deal & Nolet 1996; acts as a form of effective post hoc selection of model complexity, whilst the explicit number of parameters does not change.…”

Section: Selection Of Model Complexitymentioning

confidence: 99%

Probabilistic lowermost mantle P-wave tomography from hierarchical Hamiltonian Monte Carlo and model parametrization cross-validation

Muir

Tkalčić

2020

Geophysical Journal International

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Summary Bayesian methods, powered by Markov Chain Monte Carlo estimates of posterior densities, have become a cornerstone of geophysical inverse theory. These methods have special relevance to the deep Earth, where data is sparse and uncertainties are large. We present a strategy for efficiently solving hierarchical Bayesian geophysical inverse problems for fixed parametrisations using Hamiltonian Monte Carlo sampling, and highlight an effective methodology for determining optimal parametrisations from a set of candidates by using efficient approximations to leave-one-out cross-validation for model complexity. To illustrate these methods, we employ a case study of differential travel-time tomography of the lowermost mantle, using short period P-wave data carefully selected to minimize the contributions of the upper mantle and inner core. The resulting tomographic image of the lowermost mantle has a relatively weak degree 2 — instead there is substantial heterogeneity at all low spherical harmonic degrees less than 15. This result further reinforces the dichotomy in the lowermost mantle between relatively simple degree 2 dominated long-period S-wave tomographic models, and more complex short-period P-wave tomographic models.

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“…Contrary to ML-estimation, Bayesian inference has sometimes been used in GMM development (e.g. Wang and Takada 2009;Stafford 2019Stafford , 2014Kuehn andScherbaum 2015, 2016;Kuehn andAbrahamson 2018, 2020;Kuehn, Abrahamson, and Walling 2019;Rahpeyma et al 2018;Kowsari et al 2019Kowsari et al , 2020Ordaz, Arciniega, and Singh 1994;Arroyo andOrdaz 2010a, 2010b). In the end, GMMs can be successfully estimated using ML or Bayesian inference, and it can be a matter of convenience and familiarity which tool is chosen.…”

Section: Introductionmentioning

confidence: 99%

A Primer for using INLA to Estimate Ground-Motion Models

Kuehn¹

2021

Preprint

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Selection of earthquake ground motion models using the deviance information criterion

Cited by 23 publications

References 79 publications

A backbone seismic ground motion model for strike-slip earthquakes in Southwest Iceland and its implications for near- and far-field PSHA

A backbone seismic ground motion model for strike-slip earthquakes in Southwest Iceland and its implications for near- and far-field PSHA

Probabilistic lowermost mantle P-wave tomography from hierarchical Hamiltonian Monte Carlo and model parametrization cross-validation

A Primer for using INLA to Estimate Ground-Motion Models

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