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Quantifying the epistemic uncertainty in ground motion models and prediction |
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| Institution: | 1. State Key Laboratory of Disaster Reduction in Civil Engineering and College of Civil Engineering, Tongji University, Shanghai 200092, China;2. State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221116, China;3. School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China;4. Department of Engineering Technology, University of North Texas, Denton 76207, USA;1. Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing 100044, PR China;2. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China;3. BABRI Centre, Beijing Normal University, Beijing 100875, PR China;4. Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, PR China;1. State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China;2. State Key Laboratory for GeoMechanics and Deep Underground Engineering, Xuzhou 221008, China;3. Department of Civil and Environmental Engineering, University of Western Ontario, London, Ontario N6A 5B9, Canada |
| 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. |
| Keywords: | Earthquake ground motion Arias Intensity Epistemic uncertainty Hazard maps |
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