Capturing Geographically-Varying Uncertainty in Earthquake Ground Motion Models or What We Think We Know May Change

Abstract

Our knowledge of earthquake ground motions of engineering significance varies geographically. The prediction of earthquake shaking in parts of the globe with high seismicity and a long history of observations from dense strong-motion networks, such as coastal California, much of Japan and central Italy, should be associated with lower uncertainty than ground-motion models for use in much of the rest of the world, where moderate and large earthquakes occur infrequently and monitoring networks are sparse or only recently installed. This variation in uncertainty, however, is not often captured in the models currently used for seismic hazard assessments, particularly for national or continental-scale studies. In this theme lecture, firstly I review recent proposals for developing groundmotion logic trees and then I develop and test a new approach for application in 2-John Douglas, 16ECEE Theme Lecture Europe. The proposed procedure is based on the backbone approach with scale factors that are derived to account for potential differences between regions. Weights are proposed for each of the logic-tree branches to model large epistemic uncertainty in the absence of local data. When local data is available these weights are updated so that the epistemic uncertainty captured by the logic tree reduces. I argue that this approach is more defensible than a logic tree populated by previously published ground-motion models. It should lead to more stable and robust seismic hazard assessments that capture our doubt over future earthquake shaking.

Probabilistic seismic hazard assessment for a new-build nuclear power plant site in the UK

“…However, some recent studies have championed an alternative approach, normally referred to as "backbone" approach, where fewer GMPEs than in the traditional approach are selected (normally one or two) and epistemic uncertainty is captured by scaling up or down the median predictions of the selected GMPEs (e.g. Bommer 2012;Atkinson et al 2014;Douglas 2018).…”

A ground motion logic tree for seismic hazard analysis in the stable cratonic region of Europe: regionalisation, model selection and development of a scaled backbone approach

“…The conceptual framework of the scaled backbone logic tree that is being adopted within the ESHM20 and its data-driven regionalisation is outlined by Weatherill et al (2020), who adapt the strategy initially proposed by Douglas (2018a). In the construction of the scaled backbone GMM logic tree for general crustal seismicity Europe, Kotha et al (2020) derive a new GMM that capitalises on the wealth of strong motion data available within the European Strong Motion (ESM) flatfile (Lanzano et al 2019).…”

“…Adopting the well-established probabilistic seismic hazard analysis (PSHA) procedure and the OpenQuake software for its calculation (Pagani et al 2014), the 2020 European Seismic Hazard model (ESHM20) aims to integrate not only new insights from the exponential growth of European strong motion databases in the last decade, specifically the creation of the European Strong Motion flatfile (ESM) (Lanzano et al 2019), but also from new ideas for the representation of epistemic uncertainty that have emerged in the seismic hazard modelling community. Recognising some of the limitations of the multi-model approach, the ESHM20 has chosen to adopt instead a scaled backbone ground motion logic tree (e.g Bommer 2012; Atkinson and Adams 2013;Atkinson et al 2014), building on top of a coherent and reproducible framework for its construction initially proposed by Douglas (2018a). This concept aims to represent epistemic uncertainty in the ground motion model not by means of selecting multiple independent models, but rather by taking a core model or models (the backbone) and applying to this model(s) scaling factors to account for the uncertainty in the seismological properties of the target region.…”

“…This concept aims to represent epistemic uncertainty in the ground motion model not by means of selecting multiple independent models, but rather by taking a core model or models (the backbone) and applying to this model(s) scaling factors to account for the uncertainty in the seismological properties of the target region. A more comprehensive exposition of this approach and its advantages with respect to the multi-model approach can be found in Atkinson et al (2014), Douglas (2018a) and Weatherill et al (2020).…”

Parameters Affecting the Behaviour Factor and the Seismic Safety of Ec8-Designed Reinforced Concrete Buildings

“…In Equation 3, k is the slope of the linearized hazard curve in the logarithmic plan. In general, k is between 1 and 4 [23], dependently on the site seismic hazard, and is assumed equal to 3 in the following preliminary evaluations, as also suggested in [3]; βc is the logarithmic standard deviation of the PGA capacity. It should be determined by considering, e.g., material properties and modelling uncertainties and record-to-record variability by means of non-linear time-history analyses [24].…”