Objective Ground Motion Model Selection and Ranking Using Likelihood and Bayesian Criteria: A Case Study for Türkiye

Ground motion models (GMMs) constitute a fundamental component of the probabilistic seismic hazard analysis (PSHA) framework and represent one of the primary sources of epistemic uncertainty in seismic hazard estimates. This uncertainty largely stems from the availability of multiple candidate GMMs, with varying performance across different ground motion databases. The logic-tree framework is a well-established method for addressing this uncertainty by incorporating multiple GMMs with logic-tree branch weights. However, selecting an appropriate suite of GMMs is a non-trivial task. While often determined by expert judgment, the inherent subjectivity of this process has motivated the development of data-driven ranking methods. This study evaluates parametric and machine-learning-based GMMs for Türkiye using two information-theoretic ranking methods: the log-likelihood (LLH) and the deviance information criterion (DIC). The analysis uses an updated Turkish strong-motion database comprising 12,160 recordings from 1976 to 2023, including observations from the 2023 earthquakes. The DIC framework employs Markov chain Monte Carlo (MCMC) sampling to estimate the posterior distribution of the total residual standard deviation while keeping the median prediction functions and regression coefficients fixed. Ranking stability is further investigated across magnitude ranges and event-based subsets, alongside sensitivity of DIC to prior distributions.