A Bayesian Updating Applied to Earthquake Ground-Motion Prediction Equations for Iran

Abstract

This article presents new ground-motion prediction equations for three distinct seismic regions of Iran via updating the previous global model using observed data for each region by means of Bayesian updating. The Bayesian theory has the advantage that it results in more accurate results even in situations when little data is available. This leads the way for updating global models to obtain new local models for seismotectonic regions with little available data like Iran. The proposed updated model was compared against currently available models for Iran and the results reveal the overall stability and quality performance of the proposed model.

Keywords:

• Ground-Motion
• Prediction Equation
• Bayesian Updating
• Uncertainty
• Ranking

Acknowledgments

We thank the Building and Housing Research Centre of Iran for providing the accelerographic database. The authors would like to thank anonymous reviewers for comments which helped to improve the article.