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We analyse two datasets of hydraulic conductivity (K) from the MAcroDispersion Experiment (MADE) site, one measured by direct-push injection logging (DPIL) and the other by flowmeter profiling. The analysis is performed using copula techniques which do not rely on the assumption of multivariate Gaussianity and provide a means to characterise differing degrees of spatial dependence in different quantiles of the K distribution. This characterisation provides better insights into the similarities and differences between the two datasets. In addition to the marginal distributions and the traditional two-point geostatistical measures, copula-based bivariate rank correlation and asymmetry measures are analysed and compared. Furthermore, the parameter estimates obtained by likelihood estimation using n-point theoretical models are analysed. This analysis confirms the similarity of the spatial dependence of K between the two datasets in terms of their marginal distributions and bivariate measures, particularly in the vertical direction. We demonstrate clear indications of the existence of non-Gaussian spatial dependence structures of K at this site. We were able to improve the estimation of the K distribution by taking into account either non-Gaussianity or a censoring threshold, which are expected to lead to a more realistic description of processes that are dependent on K.

Comparison of Multivariate Spatial Dependence Structures of DPIL and Flowmeter Hydraulic Conductivity Data Sets at the MADE Site