Articles | Volume 11, issue 7
Methods for assessment of models
31 Jul 2018
Methods for assessment of models |  | 31 Jul 2018

Bayesian inference of earthquake rupture models using polynomial chaos expansion

Hugo Cruz-Jiménez, Guotu Li, Paul Martin Mai, Ibrahim Hoteit, and Omar M. Knio

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Cited articles

Abrahamson, N. A., Silva, W. J., and Kamai, R.: Summary of the ASK14 ground motion relation for active crustal regions, Earthq. Spectra, 30, 1025–1055, 2014.
Alexanderian, A., Winokur, J., Sraj, I., Srinivasan, A., Iskandarani, M., Thacker, W. C., and Knio, O. M.: Global sensitivity analysis in an ocean general circulation model: a sparse spectral projection approach, Comput. Geosci., 16, 757–778, 2012.
Arroyo, D. and Ordaz, M.: Multivariate Bayesian regression analysis applied to ground-motion prediction equations, part 1: theory and synthetic example, B. Seismol. Soc. Am., 100, 1551–1567, 2010a.
Arroyo, D. and Ordaz, M.: Multivariate Bayesian regression analysis applied to ground-motion prediction equations, Part 2: Numerical example with actual data, B. Seismol. Soc. Am., 100, 1568–1577, 2010b.
Atkinson, G. M. and Boore, D. M.: Modifications to existing ground-motion prediction equations in light of new data, B. Seismol. Soc. Am., 101, 1121–1135, 2011.
Short summary
One of the most important challenges seismologists and earthquake engineers face is reliably estimating ground motion in an area prone to large damaging earthquakes. This study aimed at better understanding the relationship between characteristics of geological faults (e.g., hypocenter location, rupture size/location, etc.) and resulting ground motion, via statistical analysis of a rupture simulation model. This study provides important insight on ground-motion responses to geological faults.