Evaluation of Empirical Ground-Motion Relations in Southern California

Abstract

Regression analysis to develop empirical ground-motion relations that predict ground-motion characteristics as a function of magnitude and distance are an essential part of seismic design and probabilistic seismic hazard assessment. Several different ground-motion relations have been presented based on different data and assumptions. This study evaluates five that were judged likely to be appropriate for southern California. We test them against the strong-motion data recorded in this region between 1933 and 1994, for magnitudes between 5.0 and 7.5, and for distances up to 150 km. The ground-motion parameters examined are peak acceleration and response spectral acceleration, SA, at 0.3-, 1.0-, and 3.0-sec periods. Some of the ground-motion relations are more consistent than others with the limited set of southern California data, but none of them stand out as either "best" or "unacceptable". Residuals from all of these models indicate that as the magnitude increases the standard deviation decreases at high frequencies but increases for SA (3.

Seismic risk assessment for oil and gas pipelines

“…The range of uncertainty can be gauged by examining the typical uncertainty in estimating ground shaking. Considering only the uncertainty in attenuation and adopting the range of standard deviations in ground motion © Woodhead Publishing Limited, 2013 estimates presented in Lee et al (2000), for a predicted peak ground acceleration value of 0.5 g, the actual peak ground acceleration would lie within 0.4 g to 1.0 g, 50% of the time. The actual variation would be larger if variability other than that associated with attenuation were considered.…”

A Survey of Techniques for Predicting Earthquake Ground Motions for Engineering Purposes

“…Still lack of near-source records from large events (hence difficult to know if observations are well representative of the true range of possible motions or sampling artifact); difficult to find records to match scenario characteristics in addition to magnitude and distance; small databanks for most regions (outside California and Japan); often implicit assumption is that host and target regions have similar characteristics (or that strong motions are not dependent on region); difficult to ascertain whether certain records are applicable elsewhere due to particular site or source effects; scaling can have significant impact on results of dynamic analyses Esteva and Rosenblueth (1964), Trifunac (1976), Joyner and Boore (1988), Abrahamson and Shedlock (1997), Anderson (1997b), Lee et al (2000), Campbell (2002), Douglas (2003), Scherbaum et al (2004), Bommer and Alarćon (2006), Power et al (2008), Abrahamson et al (2008) Available Output is strong-motion parameter rather than time-history; strong-motion parameter is not always useful for sophisticated engineering analyses; still lack of near-source records from large events (hence difficult to know if observations are well representative of the true range of possible motions or sampling artifact); small databanks for most regions (outside California and Japan); often implicit assumption is that host and target regions have similar characteristics (or that strong motions are not dependent on region); applies to a generic (mainly unknown) situation so cannot account for site-specific conditions; never sure of having the correct functional form; observed data smoothed due to large scatter in observations; requires lots of records to derive models; at edges of dataspace predictions poorly constrained; physically basis of coefficients is not always clear; ground motions from small and large events scale differently with magnitude and distance hence difficult to use weak records to predict strong motions; debate over preference for global, regional or local models; large epistemic uncertainty, mainly due to limited data Surv Geophys (2008) 29:187-220 191 Cancani (1904), Gutenberg and Richter (1942), Hershberger (1956), Ambraseys (1974), Trifunac and Brady (1975), Murphy and Ó Brien (1977), Campbell (1986), Wald et al (1999), Atkinson and Sonley (2000), …”

Direct Calculation of the Probability Distribution for Earthquake Losses to a Portfolio

“…The loss function for the small house is conditioned on peak ground acceleration (PGA), while that for the large house is conditioned on 0.2 sec spectral acceleration (SA). We use the ground motion relations of Boore et al (1997) for these ground motion measures for the site condition at each site, but we use the estimates of intra and inter given by Lee et al (2000). (We apply their estimates for 0.3 sec SA to 0.2 sec SA.)…”

“…We compute the probabilistic annual losses for the bridge inventory using a log standard deviation of 0.75 for ground-motion variability. We assume that interevent variability is half the intra-event variability, based on the findings of Lee et al (2000), yielding values of inter = 0.335 and intra = 0.671.…”