A semiparametric Bayesian method with birth-death Markov Chain Monte Carlo algorithm for extreme mooring tension analysis

Abstract

A series of 1: 50 model tests have been conducted to study the dynamics of a taut mooring system and two hybrid mooring systems attached to a deep-water semi-submersible in the 100-year wave condition of the South China Sea. Three typical environmental conditions were tested, including head sea, beam sea and quartering sea. The mixture Gamma- Generalized Pareto distribution (GPD) model is applied to study short term extreme dynamic mooring tensions based on the measured data, the results are compared with the estimations from mixture Gamma and two-parameter Weibull models. A birth-death Markov chain Monte Carlo (MCMC) sampling procedure is developed to fit the parameters of a mixture Gamma- GPD via Bayesian inference, and an extensive discussion of the de-clustering procedure, parameters of the prior distribution for threshold and MCMC iteration steps on Bayesian inference are carried out. It is found that the Weibull model underestimates the most probable largest dynamic mooring tensions. On the contrary, the mixture Gamma model overestimates the extreme dynamic mooring tension significantly. The mixture Gamma- GPD can present good estimations of extreme dynamic mooring tension, and its performance is robust to the sample definition, MCMC iteration steps and parameters of threshold prior distribution.