Effect of the Sampling Network on Estimates of the Covariance Function of Stationary Fields

Abstract

AbstractThe covariance function of the stochastic component of various soil properties plays an essential role in the estimation and interpolation of spatial phenomena using kriging techniques and in the solution of transport problems in heterogeneous media using stochastic continuum transport modeling. In this study the effect of the sampling network on the estimates of covariance functions of two‐dimensional, isotropic, second‐order stationary processes, characterized by different underlying correlation scales, was analyzed. Estimates of the covariance function based on a standard systematic sampling network were compared with estimates of the covariance functions based on a modified sampling network. Results of these comparisons showed that the modified sampling network was better suited than the systematic network to resolving the statistical properties of the underlying stochastic process at small separation distances. This may result in an improvement in structure identification. Analyses of the effect of the sampling network used for estimating the covariance function on the results of conditional inference procedures such as kriging showed that the kriging estimates were essentially insensitive to the geometric configuration of the sampling network. Uncertainties about these estimates were reduced when using the modified sampling network, however. In the case of unconditional inferences, such as with stochastic analyses of transport problems in heterogeneous media, predictions of the head variance provided by using estimates of the parameters of the covariance function of the log saturated hydraulic conductivity based on the modified sampling network fit the theoretical head variance better than those based on the systematic sampling network. The effect of the sampling network used for estimating the covariance function on the predicted head variance was significant, particularly when the underlying correlation scale was relatively small, when the porous medium texture became finer, and when the hydraulic conductivity variations were treated as being three‐dimensional.