Abstract
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.
Highlights
Probabilistic Seismic Hazard Assessment (PSHA) has often been shown to be strongly influenced by the uncertainty in strong ground motion estimation, especially at long return periods, i.e., low annual rates of exceedance (e.g. Bommer and Abrahamson 2006)
We show that global ground motion prediction equation (GMPE) may still capture uSS, provided that: (1) the magnitude scaling errors are accommodated by the event terms; (2) there are no distance scaling errors
As strong ground motion data sets have rapidly augmented during the past decades, it has been made possible to test the hypothesis of ergodicity and it appears that variability of strong ground motion at a specific site, commonly referred to as single-station sigma, rSS, is usually much lower that the variability of global GMPEs (e.g. Chen and Tsai 2002; Atkinson 2006; Morikawa et al 2008; Rodriguez-Marek et al 2011, 2013)
Summary
Probabilistic Seismic Hazard Assessment (PSHA) has often been shown to be strongly influenced by the uncertainty in strong ground motion estimation, especially at long return periods, i.e., low annual rates of exceedance (e.g. Bommer and Abrahamson 2006). According to Anderson and Brune (1999), this is because part of the variability in ground motion is due to path and site effects, which may be repeated in subsequent earthquakes Identifying and quantifying this epistemic fraction could optimally lead to a decrease in r and to more realistic PSHA results. As strong ground motion data sets have rapidly augmented during the past decades, it has been made possible to test the hypothesis of ergodicity and it appears that variability of strong ground motion at a specific site, commonly referred to as single-station sigma, rSS, is usually much lower that the variability of global GMPEs This study shows that these approaches yield comparable uSS This is a test not performed before, and important for cases where not enough data is available to create a local model, or when site-specific PSHA is performed
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