Simulation of Closed Skew Normal and Closed Skew-T Distributions for Bayesian Seismic Inversion Model

Skew spatial data often are modeled by using skew Gaussian random field. The main problem is that simulations from this random field are very time consuming for some parameter values and large dimensions. Also it is impossible in some cases and requires using of an approximation methods. One a spatial statistics branch often used to determine the natural resources such as oil and gas, is analysis of seismic data by inverse model. Bayesian Gaussian inversion model commonly is used in seismic inversion that the analytical and computational can easily be done for large dimensions. But in practice, we are encountered with the variables that are asymmetric and skewed. They are modeled using skew distributions. In Bayesian Analysis of closed skew Gaussian inversion model, there is an important problem to generate samples from closed skew normal distributions. In this paper, an efficient algorithm for the realization of the Closed Skew Normal Distribution is provided with higher dimensions. Also the Closed Skew T Distribution is offered that include heavy tails in the density function and the simulation algorithm for generating samples from the Closed Skew T Distribution is provided. Finally, the discussion and conclusions are presented.