Second, these realizations are permuted in a way such that the correlation of the field is accurately represented. First, Latin hypercube sampling (LHS) is used to select realizations from the probability density function (pdf). There are two main steps in the proposed method. The proposed method is compared with three other random field generation algorithms: sequential Gaussian simulation available in GSLIB, the turning-bands method, and LU decomposition. The purpose of this paper is to report on a new approach to the selection of Monte Carlo realizations that results in a reduction in the computational effort required to achieve a random-field simulation of groundwater flow and transport. Hence a reduction in the computational effort is realized. In this procedure, any reduction in the number of realizations of hydraulic conductivity required to achieve a specified statistical accuracy will directly reduce the number of flow and transport problems that must be solved. Realizations of hydraulic head and contaminant concentration can be obtained as output from the model and the relevant statistics calculated. Given a probabilistic description of hydraulic conductivity, random field realizations can be generated and used as input to numerical models. The Monte Carlo technique is often used to numerically simulate groundwater flow and mass transport when hydraulic conductivity is described as a random field.
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