Quantifying the epistemic uncertainty in ground motion models and prediction: Fid-Bau Portal

Quantifying the epistemic uncertainty in ground motion models and prediction

Foulser-Piggott, R.

Abstract The aim of this paper is to compute the ground-motion prediction equation (GMPE)-specific components of epistemic uncertainty, so that they may be better understood and the model standard deviation potentially reduced. The reduced estimate of the model standard deviation may also be more representative of the true aleatory uncertainty in the ground-motion predictions. The epistemic uncertainty due to input variable uncertainty and uncertainty in the estimation of the GMPE coefficients are examined. An enhanced methodology is presented that may be used to analyse their impacts on GMPEs and GMPE predictions. The impacts of accounting for the input variable uncertainty in GMPEs are demonstrated using example values from the literature and by applying the methodology to the GMPE for Arias Intensity. This uncertainty is found to have a significant effect on the estimated coefficients of the model and a small effect on the value of the model standard deviation. The impacts of uncertainty in the GMPE coefficients are demonstrated by quantifying the uncertainty in hazard maps. This paper provides a consistent approach to quantifying the epistemic uncertainty in hazard maps using Monte Carlo simulations and a logic tree framework. The ability to quantify this component of epistemic uncertainty offers significant enhancements over methods currently used in the creation of hazard maps as it is both theoretically consistent and can be used for any magnitude–distance scenario.

Highlights • Quantification of GMPE-specific components of epistemic uncertainty. • New methodology to analyse impacts of uncertainty on GMPEs and GMPE predictions. • Two example applications of the methodology are given (1) application of the methodology to a GMPE for Arias Intensity and (2) quantification of the uncertainty in hazard maps.