The Relative Importance of Head, Flux and Prior Information in Hydraulic Tomography Analysis

Abstract:

Using cross-correlation analysis, we demonstrate that flux measurements at observation locations during hydraulic tomography (HT) surveys carry non-redundant information about heterogeneity that are complementary to head measurements at the same locations. We then hypothesize that a joint interpretation of head and flux data, even when the same observation network as head is used, can enhance the resolution of HT estimates. Subsequently, we use numerical experiments to test this hypothesis and investigate the impact of flux conditioning and prior information such as correlation lengths, and initial mean models (uniform or distributed means) on the HT estimates of a non-stationary hydraulic conductivity field. We find that the addition of flux conditioning to HT analysis improve the estimates in all of the prior models tested. While prior information (as uniform mean or layered means, correlation scales) could be useful, its influence on the estimates moderates as more non-redundant data (i.e. flux) are used in the HT analysis. Lastly, some recommendations for conducting HT surveys and analysis are presented.