- Ground-motion model is that of Chiou and Youngs (2008) (see Section 2.293). Also uses same data. This
model selected since sufficiently complete and readily available at time of analysis.
- Notes that most GMPEs treat input variables as exact, neglecting uncertainties associated with
measurements of V s, Mw and r. These uncertainties propagate through regression and result in model
overestimating inherent variability in ground motion. Presents method to estimate uncertainty of input
parameters and incorporate it into regression procedure using Bayesian framework.
- Follows on from Moss and Der Kiureghian (2006) (see Section 2.263).
- Presents the Bayesian framework used for regression. This procedure is iterative and leads to results that
are slightly non-unique.
- Uses the functional form and data of Boore et al. (1997) for feasibility study. Repeat analysis of Boore
et al. (1997) and confirm published results. Then assumes uncertainties on V s,30 and rjb of coefficient of
variation (COV) of 15% and find that intra-event σ reduces by 15 and 17% respectively. Also introduces
uncertainty of standard deviation of 0.1 on Mw and finds inter-event σ reduces by 20%. Overall finds
reduction of 37%. Finds that coefficients obtained are similar to those found with standard regression.
- Discusses in detail the epistemic uncertainties associated with measurements of V s and the procedures and
data used to quantify intra- and inter-method variabilities of measurement techniques. Conclusions are
used to estimate standard deviations for each measurement of V s,30 based on the measurement method,
soil type and V s,30 and possible bias in measurements are corrected using derived empirical formulae.
- Briefly discusses epistemic uncertainties associated with estimates of Mw. Plots standard deviations of
Mw estimates w.r.t. Mw for NGA database. Finds negative correlation, which relates to a number of
factors. Regression on data gives σM_M = -0.1820ln(M)+0.4355, which is combined with reported time
component of standard deviation σMt = 0.081 thus: σM = to give the overall uncertainty
in Mw. Notes that more work is needed to quantify uncertainty in Mw. Does not include the uncertainty
in Mw in regression results.
- Discusses epistemic uncertainties in source-to-site distances and estimates different components of
uncertainty. Notes that more work is needed to quantify uncertainties and, therefore, does not account for
this uncertainty in regression.
- Replicates results reported by Chiou and Youngs (2008). Then assumes an average V s,30 measurement
uncertainty of COV≈ 27% and reports the decrease in σ (4%).
- Compare results to approximate solutions from first-order second-moment and Monte Carlo techniques,
which are useful since they are quicker than the full Bayesian regression. Find reasonable match in results.
- Notes that the smaller σs could have a large impact on PSHAs for long return periods.