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\begin{document}

\title{Estimation of Hydraulic Conductivity in a Watershed Using Multi-source
Data via Co-Kriging and Bayesian Experimental Design}



\author[1]{Maryam Ghadiri}%
\author[2]{Chien-Yung Tseng}%
\author[3]{Timothy H Larson}%
\author[2]{Praveen Kumar}%
\author[2]{Hadi Meidani}%
\affil[1]{University of Illinois at Urbana Champaign}%
\affil[2]{University of Illinois at Urbana-Champaign}%
\affil[3]{Illinois State Geological Survey}%


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  \date{May 25, 2024}


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\begin{abstract}
Enhanced water management systems depend on accurate estimation of
hydraulic properties of subsurface formations. This is while hydraulic
conductivity of geologic formations could vary significantly. Therefore,
using information only from widely spaced boreholes will be insufficient
in characterizing subsurface aquifer properties. Hence, there is a need
for other sources of information to complement our hydro-geophysics
understanding of a region of interest. This study presents a numerical
framework where information from different measurement sources is
combined to characterize the 3-dimensional random field representing the
hydraulic conductivity of a watershed in a Multi-Fidelity estimation
model. Coupled with this model, a Bayesian experimental design will also
be presented that is used to select the best future sampling locations.
This work draws upon unique capabilities of electrical resistivity tests
as well as statistical inversion. It presents a Multi-Fidelity Gaussian
Processes (Kriging) model to estimate the geological properties in Upper
Sangamon Watershed in east central Illinois, using multi-source
observation data, obtained from electrical resistivity and pumping
tests. We demonstrate the accuracy of Co-Kriging that is dependent on
the locations and the distribution of both the high- and low-fidelity
data, and also discuss its comparison with Single-High-Fidelity Kriging
results. The uncertainties and confidence in the measurements and
parameter estimates are then quantified and are in turn used to design
future cycles of data collection to further improve the confidence
intervals.%
\end{abstract}%



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\textbf{Hosted file}

\verb`essoar.10509901.1.docx`  available at \url{https://authorea.com/users/523296/articles/596116-estimation-of-hydraulic-conductivity-in-a-watershed-using-multi-source-data-via-co-kriging-and-bayesian-experimental-design}

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