Bayesian Analysis of Peak Ground Acceleration Attenuation Relationship

Estimation of peak ground acceleration is one of the main issues in civil and earthquake engineering practice. The Boore-Joyner-Fumal empirical formula [1] is well known for this purpose. In this paper we propose to use the Bayesian probabilistic model class selection approach to obtain the most suitable prediction model class for the seismic attenuation formula. The optimal model class is robust in the sense that it has balance between the data fitting capability and the sensitivity to noise. A database of strong-motion records is utilized for the analysis. It turns out that the optimal model class is simpler than the full order attenuation model suggested by Boore, Joyner and Fumal (1993).