Application of nested sampling algorithm for assessing the uncertainty in groundwater flow model

The model evaluation (model selection) is an important research content of uncertainty analysis of groundwater numerical simulation, and marginal likelihood of a model is an essential basis for model evaluation. Nested sampling algorithm is an efficient high-dimensional integral method, which can effectively calculate the marginal likelihood of complex model. The nested sampling algorithm based on Adaptive Metropolis was proposed in this study, by calculating the marginal likelihoods of two (linear, non-linear) analytic functions and a set of groundwater models with different structures, and compared with the results of the arithmetic average method under the condition of large sample, the validity of the method was verified. The results show that the nested sampling algorithm has high calculation accuracy and computational efficiency, and is an effective model evaluation method.