Monitoring Unsaturated Flow and Transport Using Cross‐Borehole Geophysical Methods

Looms, Majken C.; Jensen, Karsten H.; Binley, Andrew; Nielsen, Lars

doi:10.2136/vzj2006.0129

Cited by 118 publications

(135 citation statements)

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“…The recorded EM signal provides information on the electrical permittivity and the electrical conductivity of the investigated formation, that can be related to velocity and the attenuation of the EM wave, respectively (Annan, 2005). To date, published field studies using crosshole GPR only include sediments with low electrical conductivity, e.g., unconsolidated sands (Cassiani et al, 2006;Irving et al, 2007;Haarder et al, 2015;Looms et al, 2008;Klotzsche et al, 2010;Gueting et al, 2015), sandstone (Binley et al, 2002), and chalk (Keskinen et al, 2017). In general, clayey environments have been avoided as the attenuation of the EM signal reduces the signal strength and thereby the possible distance between boreholes.…”

Section: Introductionmentioning

confidence: 99%

Mapping sand layers in clayey till using crosshole ground-penetrating radar

et al. 2018

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Fluid transport through clayey tills governs the quantity and quality of groundwater resources in the Northern hemisphere. This transport is often controlled by a three-dimensional network of macropores (biopores, fractures and sand lenses) within the clayey till. At present, a non-destructive technique that can map and characterize the sand-lens-network does not exist, and full excavation or extensive drilling is therefore the only solution. Acquisition and modeling of crosshole ground penetrating radar (GPR) may provide the answer to this problem. We collected one-and two-dimensional crosshole GPR data at a field site in Denmark between four 8-m-deep boreholes with horizontal distances varying between 2.64-5.05 m. We show that the depth, thickness, and tilt of a coherent sand layer within the clayey till (approximately 0.4-0.6 m thick) as well as the underlying sand formation can be mapped accurately using the GPR data.We identified the sand efficiently as a highly resistive section with high electromagnetic wave velocities, while the clayey till was conductive with lower electromagnetic wave velocities. We found that the exact location of the sand occurrences was better delineated by the increase in amplitude than the increase in electromagnetic wave velocity. We believe that crosshole GPR may contribute significantly to groundwater protection and contaminant remediation initiatives.

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Section: Introductionmentioning

confidence: 99%

Mapping sand layers in clayey till using crosshole ground-penetrating radar

et al. 2018

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“…As resistivity depends on properties such as saturation, solute concentration and temperature, timelapse ERT can be used to monitor natural and anthropogenic processes that cause changes in these properties, such as infiltration (Daily et al, 1992;Looms et al, 2008), saline intrusion (Slater and Sandberg, 2000;Ogilvy et al, 2007;, leachate recirculation (Guerin et al, 2004), and contaminated land remediation (Daily and Ramirez, 1995;LaBrecque et al, 1996;Slater and Binley, 2003;Halihan et al, 2005.…”

Section: Introductionmentioning

confidence: 99%

“…The most common approach is to monitor cross-hole resistivity data and use 2D inversion algorithms to generate images in the borehole planes (e.g. 1995;LaBrecque et al, 1996;Slater et al, 1997;Sandberg et al, 2002;Kemna et al, 2002;Deiana et al, 2007;Looms et al, 2008). 2D inversion is rapid, permitting monitoring on timescales of tens of minutes to a few hours, but provides information only in the plane between the boreholes and is prone to artefacts caused by off-plane 3D features (Nimmer et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…This method has been used to monitor tracer tests on timescales of several hours to a few days, typically using fewer than 10 boreholes, each with some 10 -20 electrodes (Daily and Ramirez, 2000;Binley et al, 2002;Singha and Gorelick, 2005;Oldenborger et al, 2007a;Kuras et al, 2009). In many cases, quantitative estimates can be made of seepage velocities (Sandberg et al, 2002), spatial moments (Binley et al, 2002;Singha and Gorelick, 2005;Looms et al, 2008), hydraulic conductivity (Binley et al, 2002), and tracer mass and concentration (Singha and Gorelick, 2006;Oldenborger et al, 2007a).…”

Section: Introductionmentioning

confidence: 99%

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High-resolution Electrical Resistivity Tomography monitoring of a tracer test in a confined aquifer

Wilkinson

Meldrum

Kuras

et al. 2010

Journal of Applied Geophysics

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A permanent geoelectrical subsurface imaging system has been installed at a contaminated land site to monitor changes in groundwater quality after the completion of a remediation programme. Since the resistivities of earth materials are sensitive to the presence of contaminants and their break-down products, 4-dimensional resistivity imaging can act as a surrogate monitoring technology for tracking and visualising changes in contaminant concentrations at much higher spatial and temporal resolution than manual intrusive investigations. The test site, a municipal car-park built on a former gas-works, had been polluted by a range of polycyclic aromatic hydrocarbons and dissolved phase contaminants. It was designated statutory contaminated land under Part IIA of the UK Environmental Protection Act due to the risk of polluting an underlying minor aquifer. Resistivity monitoring zones were established on the boundaries of the site by installing vertical electrode arrays in purpose-drilled boreholes. After a year of monitoring data had been collected, a tracer test was performed to investigate groundwater flow velocity and to demonstrate rapid volumetric monitoring of natural attenuation processes. A saline tracer was injected into the confined aquifer, and its motion and evolution were visualised directly in high-resolution tomographic images in near real-time. Breakthrough curves were calculated from independent resistivity measurements, and the estimated seepage velocities from the monitoring images and the breakthrough curves were found to be in good agreement with each other and with estimates based on the piezometric gradient and assumed material parameters.

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“…However, for 2-layered or soil moisture profile models, the soil moisture estimation at the lower layers was not adequately accurate due to the illposed inverse problem caused by too many unknown parameters and the low sensitivity of GPR data with the electrical properties at deeper layers (Lambot et al, 2004a;Minet et al, 2011b). Efforts have been made to increase the accuracy of the soil moisture profile estimation by using the time-lapse GPR data to constrain a soil hydrodynamic model (Kowalsky et al, 2005;Lambot et al, 2006Lambot et al, , 2009Jadoon et al, 2008Jadoon et al, , 2012Looms et al, 2008;Dagenbach et al, 2013). A joint inversion procedure based on integrated geophysical and hydrodynamic models was demonstrated to optimally estimate the soil unsaturated hydraulic parameters.…”

Section: Introductionmentioning

confidence: 99%

Improving soil moisture profile reconstruction from ground-penetrating radar data: a maximum likelihood ensemble filter approach

Tran

Vanclooster

Lambot

2013

Hydrol. Earth Syst. Sci.

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Abstract. The vertical profile of shallow unsaturated zone soil moisture plays a key role in many hydro-meteorological and agricultural applications. We propose a closed-loop data assimilation procedure based on the maximum likelihood ensemble filter algorithm to update the vertical soil moisture profile from time-lapse ground-penetrating radar (GPR) data. A hydrodynamic model is used to propagate the system state in time and a radar electromagnetic model and petrophysical relationships to link the state variable with the observation data, which enables us to directly assimilate the GPR data. Instead of using the surface soil moisture only, the approach allows to use the information of the whole soil moisture profile for the assimilation. We validated our approach through a synthetic study. We constructed a synthetic soil column with a depth of 80 cm and analyzed the effects of the soil type on the data assimilation by considering 3 soil types, namely, loamy sand, silt and clay. The assimilation of GPR data was performed to solve the problem of unknown initial conditions. The numerical soil moisture profiles generated by the Hydrus-1D model were used by the GPR model to produce the "observed" GPR data. The results show that the soil moisture profile obtained by assimilating the GPR data is much better than that of an open-loop forecast. Compared to the loamy sand and silt, the updated soil moisture profile of the clay soil converges to the true state much more slowly. Decreasing the update interval from 60 down to 10 h only slightly improves the effectiveness of the GPR data assimilation for the loamy sand but significantly for the clay soil. The proposed approach appears to be promising to improve real-time prediction of the soil moisture profiles as well as to provide effective estimates of the unsaturated hydraulic properties at the field scale from time-lapse GPR measurements.

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Monitoring Unsaturated Flow and Transport Using Cross‐Borehole Geophysical Methods

Cited by 118 publications

References 32 publications

Mapping sand layers in clayey till using crosshole ground-penetrating radar

Mapping sand layers in clayey till using crosshole ground-penetrating radar

High-resolution Electrical Resistivity Tomography monitoring of a tracer test in a confined aquifer

Improving soil moisture profile reconstruction from ground-penetrating radar data: a maximum likelihood ensemble filter approach

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