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

Abstract

Regions of low seismicity present a particular challenge for probabilistic seismic hazard analysis when identifying suitable ground motion models (GMMs) and quantifying their epistemic uncertainty. The 2020 European Seismic Hazard Model adopts a scaled backbone approach to characterise this uncertainty for shallow seismicity in Europe, incorporating region-to-region source and attenuation variability based on European strong motion data. This approach, however, may not be suited to stable cratonic region of northeastern Europe (encompassing Finland, Sweden and the Baltic countries), where exploration of various global geophysical datasets reveals that its crustal properties are distinctly different from the rest of Europe, and are instead more closely represented by those of the Central and Eastern United States. Building upon the suite of models developed by the recent NGA East project, we construct a new scaled backbone ground motion model and calibrate its corresponding epistemic uncertainties. The resulting logic tree is shown to provide comparable hazard outcomes to the epistemic uncertainty modelling strategy adopted for the Eastern United States, despite the different approaches taken. Comparison with previous GMM selections for northeastern Europe, however, highlights key differences in short period accelerations resulting from new assumptions regarding the characteristics of the reference rock and its influence on site amplification.

Probabilistic Seismic Hazard Assessment of Sweden

“…This study provides a probabilistic seismic hazard model of Sweden based on updated earthquake catalogues from the Swedish national seismic network (Lund et al, 2021) and neighbouring networks, and recent results and methods from the European seismic hazard model ESHM20 (Danciu et al, 2021). The hazard is calculated using the OpenQuake engine (Pagani et al, 2014), using earthquakes since 1875 represented through 31 area sources zones, and recently developed ground motion models (Fülöp et al, 2020;Weatherill and Cotton, 2020;Kotha et al, 2020). A weighted logic tree approach is used to represent the uncertainties in the recurrence parameter estimates, M max and the ground motion models.…”

“…In this study, we use the two recently developed cratonic GMMs Fenno-G16 (Fülöp et al, 2020) and ESHM20 cratonic (Weatherill and Cotton, 2020) together with the shallow crustal seismicity model used for much of Europe in ESHM20 (Kotha et al, 2020). In order to study the effects of the different GMMs on the hazard estimates, we use our earthquake data set and seismic source zones and calculate hazard at a specific site for each of the following logic tree combinations:…”

“…V S,30 = 3 km/s for the cratonic regions. Figure 6 shows the ESHM20 five branch implementation for epistemic uncertainty of the GMM, σ µ (Weatherill and Cotton, 2020), where the ϵ ii and weights assigned to each branch are determined by a discrete approximation of the Gaussian distribution depending on the number of standard deviations to include (Danciu et al, 2021;Miller and Rice, 1983). The epistemic uncertainty of the site amplification factor, σ µ,S , is similarly included with three branches, however, as we do not adjust the site amplification to V S,30 = 800 m/s for cratonic areas these branches cancel.…”

“…We then discuss the development of a new probabilistic seismic hazard model for Sweden, which will take into account the large amount of smaller earthquakes recorded by the Fennoscandian seismic networks in the last two decades (e.g. Lund et al, 2021;Veikkolainen et al, 2021;Ottemöller et al, 2018) as well as recent developments in ground motion models (Fülöp et al, 2020;Weatherill and Cotton, 2020). The hazard is calculated using the OpenQuake engine (Pagani et al, 2014) and we produce hazard maps, with mean estimates for peak ground acceleration (PGA) corresponding to return periods of 475 and 2500 years, and hazard curves for Sweden's most seismically active areas.…”

Host-region parameters for an adjustable model for crustal earthquakes to facilitate the implementation of the backbone approach to building ground-motion logic trees in probabilistic seismic hazard analysis

“…The scaling factors may vary as a function of both magnitude and distance, but in all cases the relationship between the weights on the branches and the resulting distribution—which can be broader than that defined by existing GMM predictions—becomes much more transparent. This general approach of populating a logic tree with scaled versions of a single GMM has been called the backbone approach (Bommer, 2012) and has been widely adopted using this terminology (Akkar et al, 2021; Douglas, 2018; Haendel et al, 2015; Kowsari and Ghasemi, 2021; Weatherill and Cotton, 2020; Weatherill et al, 2020). The approach has also been adapted to avoid similar pitfalls in constructing logic trees for site response analyses (Rodriguez-Marek et al, 2020).…”