Abstract
Typical seismic ground-motion models predict the response spectral ordinates (GMM-SA), which are the damped responses of a suite of single-degree-of-freedom oscillators. Response spectra represent the response of an idealized structure to input ground-motion, but not the physics of the actual ground-motion. To complement the regionally adaptable GMM-SA of Kotha et al. (2020), we introduce a model capable of predicting Fourier amplitudes (GMM-FA); developed from the Engineering Strong Motion (ESM) dataset for pan-Europe. This GMM-FA reveals the very high variability of high frequency ground-motions, which are completely masked in a GMM-SA. By maintaining the development strategies of GMM-FA identical to that of the GMM-SA, we are able to evaluate the physical meaning of the spatial variability of anelastic attenuation and source characteristics. We find that a fully data-driven geospatial index, Activity Index (AIx), correlates well with the spatial variability of these physical effects. AIx is a fuzzy combination of seismicity and crustal parameters, and can be used to adapt the attenuation and source non-ergodicity of the GMM-FA to regions and tectonic localities sparsely sampled in ESM. While AIx, and a few other parameters we touch upon, may help understand the spatial variability of high frequency attenuation and source effects, the high frequency site-response variability—dominating the overall aleatory variance—is yet unresolvable. With the rapid increase in quantity and quality of ground-motion datasets, our work demonstrates the need to upgrade regionalization techniques, site-characterisation, and a paradigm shift towards Fourier ground-motion models to complement the traditional response spectra prediction models.
Highlights
Typical ground-motion models (GMMs) used in seismic hazard and risk assessments predict the random distribution of ground-motion in terms of spectral amplitude (SA), i.e. damped response of an elastic single-degree-of-freedom (SDOF) oscillator with fundamental resonance period T
(2020) we introduced the regionally adaptable GMM derived from the Engineering Strong-Motion
In this study we introduce a regionally adaptable GMM-FA, correlate the regional adjustments to an independently derived geospatial index, and suggest using these correlation to adapt the model to regions with little to no data in the Engineering Strong Motion (ESM) dataset
Summary
Typical ground-motion models (GMMs) used in seismic hazard and risk assessments predict the random distribution of ground-motion in terms of spectral amplitude (SA), i.e. damped response of an elastic single-degree-of-freedom (SDOF) oscillator with fundamental resonance period T. The short-period SAs integrate features of moderate-high frequency FAS , making it difficult to interpret physically the quantified random-effects at T < 0.5s Towards this end, in this study we introduce a regionally adaptable GMM-FA, correlate the regional adjustments to an independently derived geospatial index, and suggest using these correlation to adapt the model to regions with little to no data in the ESM dataset. The GMM-SA of Kotha et al (2020) is a regionally adaptable model, wherein the event and path effects are regionalised It is capable of predicting SA(T) accounting the regional differences in distance decay through Δc3,r(T) ≈ Ɲ(0, τc3(T)) and average of localised source effects through ΔL2Ll(T) ≈ Ɲ(0, τL2L(T)) , and site-specific effects through ΔS2Ss(T) ≈ Ɲ(0, φs2s(T)) random-effect adjustments to the generic GMM-SA median (ln(μ)). In this study we discuss the third approach, i.e. evaluating the 6 physical meaning and adaptability of the GMM-FA random-effects
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