Probabilistic Quantification of Hazards: A Methodology Using Small Ensembles of Physics-based Simulations and Statistical Surrogates
Document Type
Article
Language
eng
Publication Date
2015
Publisher
Begell House
Source Publication
International Journal for Uncertainty Quantification
Source ISSN
2152-5080
Abstract
This paper presents a novel approach to assessing the hazard threat to a locale due to a large volcanic avalanche. The methodology combines: (i) mathematical modeling of volcanic mass flows; (ii) field data of avalanche frequency, volume, and runout; (iii) large-scale numerical simulations of flow events; (iv) use of statistical methods to minimize computational costs, and to capture unlikely events; (v) calculation of the probability of a catastrophic flow event over the next T years at a location of interest; and (vi) innovative computational methodology to implement these methods. This unified presentation collects elements that have been separately developed, and incorporates new contributions to the process. The field data and numerical simulations used here are subject to uncertainty from many sources, uncertainties that must be properly accounted for in assessing the hazard. The methodology presented here will be demonstrated with data from the Soufriere Hills Volcano on the island of Montserrat, where there is a relatively complete record of volcanic mass flows from the past 15 years. This methodology can be transferred to other volcanic sites with similar characteristics and where sparse historical data have prevented such high-quality analysis. More generally, the core of this methodology is widely applicable and can be used for other hazard scenarios, such as floods or ash plumes.
Recommended Citation
Bayarri, M. J.; Berger, J. O.; Calder, E. S.; Patra, Abani K.; Pitman, E. Bruce; Spiller, Elaine T.; and Wolpert, Robert L., "Probabilistic Quantification of Hazards: A Methodology Using Small Ensembles of Physics-based Simulations and Statistical Surrogates" (2015). Mathematics, Statistics and Computer Science Faculty Research and Publications. 450.
https://epublications.marquette.edu/mscs_fac/450
Comments
International Journal for Uncertainty Quantification, Vol. 5, No. 4 (2015): 297-325. DOI.