Data integration is challenging where there are different levels of support between primary and secondary data that need to be correlated in various ways. A geostatistical method is described, which integrates the hydraulic conductivity (K) measurements and electrical resistivity data to better estimate the K distribution in the Upper Chicot Aquifer of southwestern Louisiana, USA. The K measurements were obtained from pumping tests and represent the primary (hard) data. Borehole electrical resistivity data from electrical logs were regarded as the secondary (soft) data, and were used to infer K values through Archie's law and the Kozeny-Carman equation. A pseudo cross-semivariogram was developed to cope with the resistivity data non-collocation. Uncertainties in the auto-semivariograms and pseudo cross-semivariogram were quantified. The groundwater flow model responses by the regionalized and coregionalized models of K were compared using analysis of variance (ANOVA). The results indicate that non-collocated secondary data may improve estimates of K and affect groundwater flow responses of practical interest, including specific capacity and drawdown. ?? Springer-Verlag 2007.
Additional publication details
Geophysical data integration, stochastic simulation and significance analysis of groundwater responses using ANOVA in the Chicot Aquifer system, Louisiana, USA