Stochastic seepage and slope stability analysis is conventionally performed in a random field framework. However, most of the studies are limited to Gaussian spatial and cross-dependence structure of hydraulic parameters. Using a well documented hydraulic conductivity (k) data from Borden aquifer, Canada (Sudicky, 1986), evidence of non-Gaussian spatial dependence is provided within a copula framework. It is shown that the non-Gaussian spatial dependence structure is a field reality for k. To handle the non-Gaussian spatial as well as cross-dependence structure, a multivariate random field framework based on vine copula theory is presented. It is shown that the vine-copula approach can efficiently model the non-Gaussian dependence structure of hydraulic parameters. For investigating the practical engineering importance of dependence structure, stochastic seepage and slope stability analysis under steady and transient seepage conditions is conducted. It is shown that the assumption of arbitrary spatial dependence structure, can significantly affect (by a factor of 100) the failure probability of slopes across the entire acceptable range (Salgado and Kim, 2014) of ${10}^{-4}$ to ${10}^{-2}$. It is also shown that the choice of spatial dependence structure is more crucial than the cross-dependence for stochastic seepage and slope stability analysis.

随机渗流和边坡稳定性分析通常在随机场框架中进行。但是，大多数研究仅限于水力参数的高斯空间和相互依赖结构。使用来自加拿大Borden含水层（Sudicky，1986）的有据可查的水力传导率（k）数据，在系谱框架内提供了非高斯空间依赖性的证据。结果表明，非高斯空间相关性结构是k的场现实。。为了处理非高斯空间以及交叉依赖结构，提出了一种基于藤蔓copula理论的多元随机场框架。结果表明，藤-copula方法可以有效地模拟水力参数的非高斯依赖结构。为了研究依存结构的实际工程重要性，进行了稳态和瞬态渗流条件下的随机渗流和边坡稳定性分析。结果表明，任意空间相关性结构的假设会显着影响（以100为因子）斜坡在整个可接受范围内的破坏概率（Salgado和Kim，2014）。${10}^{--4}$ 至 ${10}^{--2}$。研究还表明，对于随机渗流和边坡稳定性分析，空间依赖性结构的选择比交叉依赖性更为关键。