A regionally-adaptable ground-motion model for shallow crustal earthquakes in Europe
DOI: https://doi.org/10.1007/s10518-020-00869-1
Persistent URL: http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/10751
Persistent URL: http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/10751
Kotha, Sreeram Reddy; Weatherill, Graeme; Bindi, Dino; Cotton, Fabrice, 2020: A regionally-adaptable ground-motion model for shallow crustal earthquakes in Europe. In: Bulletin of Earthquake Engineering, Band 18, 9: 4091 - 4125, DOI: 10.1007/s10518-020-00869-1.
|
View/
|
To complement the new European Strong-Motion dataset and the ongoing efforts to update the seismic hazard and risk assessment of Europe and Mediterranean regions, we propose a new regionally adaptable ground-motion model (GMM). We present here the GMM capable of predicting the 5% damped RotD50 of PGA, PGV, and SA(T = 0.01 − 8 s) from shallow crustal earthquakes of 3 ≤ M W ≤ 7.4 occurring 0 < RJB ≤ 545 km away from sites with 90 ≤ Vs30 ≤ 3000 m s−1 or 0.001 ≤ slope ≤ 1 m m−1. The extended applicability derived from thousands of new recordings, however, comes with an apparent increase in the aleatory variability (σ). Firstly, anticipating contaminations and peculiarities in the dataset, we employed robust mixed-effect regressions to down weigh only, and not elimi nate entirely, the influence of outliers on the GMM median and σ. Secondly, we regionalised the attenuating path and localised the earthquake sources using the most recent models, to quantify region-specific anelastic attenuation and locality-specific earthquake characteristics as random-effects, respectively. Thirdly, using the mixed-effect variance–covariance structure, the GMM can be adapted to new regions, localities, and sites with specific datasets. Consequently, the σ is curtailed to a 7% increase at T < 0.3 s, and a sub stantial 15% decrease at T ≥ 0.3 s, compared to the RESORCE based partially non-ergodic GMM. We provide the 46 attenuating region-, 56 earthquake localities-, and 1829 site-spe cific adjustments, demonstrate their usage, and present their robustness through a 10-fold cross-validation exercise.