Understanding single-station ground motion variability and uncertainty (sigma) – Lessons learnt from EUROSEISTEST

Accelerometric data from the well-studied valley EUROSEISTEST are used to investigate ground motion uncertainty and variability. We define a simple local ground motion prediction equation (GMPE) and investigate changes in standard deviation (σ) and its components, the between-event variability (τ) and within-event variability (φ). Improving seismological metadata significantly reduces τ (30-50%), which in turn reduces the total σ. Improving site information reduces the systematic site-to-site variability, φS2S (20-30%), in turn reducing φ, and ultimately, σ. Our values of standard deviations are lower than global values from literature, and closer to path-specific than site-specific values. However, our data have insufficient azimuthal coverage for single-path analysis. Certain stations have higher ground-motion variability, possibly due to topography, basin edge or downgoing wave effects. Sensitivity checks show that 3 recordings per event is a sufficient data selection criterion, however, one of the dataset’s advantages is the large number of recordings per station (9-90) that yields good site term estimates. We examine uncertainty components binning our data with magnitude from 0.01 to 2 s; at smaller magnitudes, τ decreases and φSS increases, possibly due to κ and source-site trade-offs Finally, we investigate the alternative approach of computing φSS using existing GMPEs instead of creating an ad hoc local GMPE. This is important where data are insufficient to create one, or when site-specific PSHA is performed. We show that global GMPEs may still capture φSS, provided that: 1. the magnitude scaling errors are accommodated by the event terms; 2. there are no distance scaling errors (use of a regionally applicable model). Site terms (φS2S) computed by different global GMPEs (using different site-proxies) vary significantly, especially for hard-rock sites. This indicates that GMPEs may be poorly constrained where they are sometimes most needed, i.e. for hard rock

Modelling macroseismic observations for historical earthquakes: the cases of the M = 7.0, 1954 Sofades and M = 6.8, 1957 Velestino events (central Greece)

We attempt to model the spatial distribution of the strong ground motion for the large M = 7.0, 1954 Sofades and M = 6.8, 1957 Velestino events (southern Thessaly basin, central Greece), using the macroseismic intensities (IMM up to 9+) observed within the broader Thessaly area. For this reason, we employ a modified stochastic method realised by the EXSIM algorithm for extended sources, in order to reproduce the damage distribution of these earthquakes, in an attempt to combine existing earthquake information and appropriate scaling relations with surface geology and to investigate the efficiency of the available macroseismic data. For site-effects assessment, we use a new digital geological map of the broader Thessaly basin, where geological formations are grouped by age and mapped on appropriate NEHRP soil classes. Using the previous approach, we estimate synthetic time series for different rupture scenarios and employ various calibrating relations between PGA/PGV and macroseismic intensity, allowing the generation of synthetic (stochastic) isoseismals. Also, different site amplification factors proposed for the broader Aegean area, according to local geology, are tested. Finally, we also perform a sensitivity analysis of the fault location, taking into account the available neotectonic data for the broader southern Thessaly fault zone. The finally determined fault locations are different than previously proposed, in agreement with the available neotectonic information. The observed macroseismic intensities are in good agreement with the ones derived from the synthetic waveforms, verifying both the usefulness of the approach, as well as of the macroseismic data used. Finally, site-effects show clear correlation with the geological classification employed, with constant amplification factors for each soil class generally providing better results than generic transfer functions. © 2015, Springer Science+Business Media Dordrecht