Comparing different data-driven ranking methods and selecting appropriate GMPEs for performing PSHA in Zagros zone

Document Type : Original Article

Authors

1 Faculty of Technology and Mining, Yasouj University, Choram 75761-59836, Iran.

2 Faculty of Technology and Mining, Yasouj University, Choram 75761-59836, Iran

3 Faculty of Civil and Environmental Engineering, School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland (EERC-UI)

Abstract

In seismic hazard assessment, selection of an appropriate ground motion prediction equation (GMPE) is challenging. These models and their weights which are used in a logic tree framework to consider the epistemic uncertainty, strongly affect the seismic hazard results. In this regard, evaluating their applicability against the observed ground motions for a region under study can result in finding the most appropriate GMPE and also reducing the epistemic uncertainty. Thus, the goal of this study is to rank different GMPEs using the data-driven ranking methods for the Zagros seismotectonic province. For this purpose, three data-driven ranking methods including the log likelihood, Euclidean distance and deviance information criterion (DIC) methods were evaluated based on a simulated dataset. The results identify the DIC as the most appropriate ranking method. Then, different local, regional and worldwide candidate GMPEs were ranked using the DIC method for the Zagros seismotectonic province. For the purpose of ranking, we used a dataset that include 457 records from 99 earthquake events recorded in the Zagros seismotectonic province. The results have been calculated at seven oscillator periods. Then, the candidate GMPEs were ranked and the most appropriate GMPEs were selected. The results show the better performance of the local GMPEs compared to the regional and worldwide models. Moreover, it was concluded that the worldwide GMPEs are not suitable to use for seismic hazard assessment in the Zagros region.

Keywords

Asas Journal
Volume 22, Issue 58
Spring 2020
May 2020
Pages 35-46

Files

History

  • Receive Date: 27 May 2019
  • Revise Date: 20 September 2020
  • Accept Date: 14 October 2020

Share

How to cite

Statistics