- 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}, M_{w}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 r_{jb}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 M_{w}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 M
_{w}. Plots standard deviations of M_{w}estimates w.r.t. M_{w}for NGA database. Finds negative correlation, which relates to a number of factors. Regression on data gives σ_{M_M}= -0.1820ln(M)+0.4355, which is combined with reported time component of standard deviation σ_{Mt}= 0.081 thus: σ_{M}= to give the overall uncertainty in M_{w}. Notes that more work is needed to quantify uncertainty in M_{w}. Does not include the uncertainty in M_{w}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.