Reflection tomography of time-lapse GPR data for studying dynamic unsaturated flow phenomena

<p>Ground-penetrating radar (GPR) reflection tomography algorithms allow non-invasive monitoring of water content changes resulting from flow in the vadose zone. The approach requires multi-offset GPR data that are traditionally slow to collect. We automate GPR data collection to reduce the su...

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Main Authors: A. R. Mangel, S. M. J. Moysey, J. Bradford
Format: Article
Language:English
Published: Copernicus Publications 2020-01-01
Series:Hydrology and Earth System Sciences
Online Access:https://www.hydrol-earth-syst-sci.net/24/159/2020/hess-24-159-2020.pdf
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spelling doaj-a42aedd23560442fae171cc3e75aab732020-11-25T01:39:09ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382020-01-012415916710.5194/hess-24-159-2020Reflection tomography of time-lapse GPR data for studying dynamic unsaturated flow phenomenaA. R. Mangel0A. R. Mangel1S. M. J. Moysey2J. Bradford3Department of Geophysics, Colorado School of Mines, Golden, Colorado 80401, USADepartment of Environmental Engineering and Earth Science, Clemson University, Clemson, South Carolina 29634, USADepartment of Environmental Engineering and Earth Science, Clemson University, Clemson, South Carolina 29634, USADepartment of Geophysics, Colorado School of Mines, Golden, Colorado 80401, USA<p>Ground-penetrating radar (GPR) reflection tomography algorithms allow non-invasive monitoring of water content changes resulting from flow in the vadose zone. The approach requires multi-offset GPR data that are traditionally slow to collect. We automate GPR data collection to reduce the survey time significantly, thereby making this approach to hydrologic monitoring feasible. The method was evaluated using numerical simulations and laboratory experiments that suggest reflection tomography can provide water content estimates to within 5&thinsp;%&thinsp;vol&thinsp;vol<span class="inline-formula"><sup>−1</sup></span>–10&thinsp;%&thinsp;vol&thinsp;vol<span class="inline-formula"><sup>−1</sup></span> for the synthetic studies, whereas the empirical estimates were typically within 5&thinsp;%–15&thinsp;% of measurements from in situ probes. Both studies show larger observed errors in water content near the periphery of the wetting front, beyond which additional reflectors were not present to provide data coverage. Overall, coupling automated GPR data collection with reflection tomography provides a new method for informing models of subsurface hydrologic processes and a new method for determining transient 2-D soil moisture distributions.</p>https://www.hydrol-earth-syst-sci.net/24/159/2020/hess-24-159-2020.pdf
collection DOAJ
language English
format Article
sources DOAJ
author A. R. Mangel
A. R. Mangel
S. M. J. Moysey
J. Bradford
spellingShingle A. R. Mangel
A. R. Mangel
S. M. J. Moysey
J. Bradford
Reflection tomography of time-lapse GPR data for studying dynamic unsaturated flow phenomena
Hydrology and Earth System Sciences
author_facet A. R. Mangel
A. R. Mangel
S. M. J. Moysey
J. Bradford
author_sort A. R. Mangel
title Reflection tomography of time-lapse GPR data for studying dynamic unsaturated flow phenomena
title_short Reflection tomography of time-lapse GPR data for studying dynamic unsaturated flow phenomena
title_full Reflection tomography of time-lapse GPR data for studying dynamic unsaturated flow phenomena
title_fullStr Reflection tomography of time-lapse GPR data for studying dynamic unsaturated flow phenomena
title_full_unstemmed Reflection tomography of time-lapse GPR data for studying dynamic unsaturated flow phenomena
title_sort reflection tomography of time-lapse gpr data for studying dynamic unsaturated flow phenomena
publisher Copernicus Publications
series Hydrology and Earth System Sciences
issn 1027-5606
1607-7938
publishDate 2020-01-01
description <p>Ground-penetrating radar (GPR) reflection tomography algorithms allow non-invasive monitoring of water content changes resulting from flow in the vadose zone. The approach requires multi-offset GPR data that are traditionally slow to collect. We automate GPR data collection to reduce the survey time significantly, thereby making this approach to hydrologic monitoring feasible. The method was evaluated using numerical simulations and laboratory experiments that suggest reflection tomography can provide water content estimates to within 5&thinsp;%&thinsp;vol&thinsp;vol<span class="inline-formula"><sup>−1</sup></span>–10&thinsp;%&thinsp;vol&thinsp;vol<span class="inline-formula"><sup>−1</sup></span> for the synthetic studies, whereas the empirical estimates were typically within 5&thinsp;%–15&thinsp;% of measurements from in situ probes. Both studies show larger observed errors in water content near the periphery of the wetting front, beyond which additional reflectors were not present to provide data coverage. Overall, coupling automated GPR data collection with reflection tomography provides a new method for informing models of subsurface hydrologic processes and a new method for determining transient 2-D soil moisture distributions.</p>
url https://www.hydrol-earth-syst-sci.net/24/159/2020/hess-24-159-2020.pdf
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