International audienceAn understanding of the heterogeneity of quaternary gravelly deposits is required to predict flow and contaminant transfer through these formations. In such deposits, preferential flow paths can lead to contamination at depths greater than predicted under the assumption of a homogeneous medium. The difficulties in characterizing their complex structure with conventional methods represent an obstacle for this prediction. In this study, we developed an approach relying on the use of ground penetrating radar (GPR) for the detection of sedimentary depositional units. A genetic interpretation of the radar stratigraphy allowed us to construct a distribution model of lithofacies. The study was conducted on glaciofluvial deposits underlying a stormwater infiltration basin. Two main system tracts were characterized: a top stratum (50–80 cm deep) corresponding to massive gravel and open-framework gravel, and a base stratum corresponding to trough-fill structures with associated sandy, open-framework, massive, and matrix-rich gravelly lithofacies. The knowledge of the hydraulic properties linked to each lithofacies led us to propose a hydrostratigraphic model. Based on this model, we formulated a hypothesis about the hydraulic behavior of the deposit during stormwater infiltration. Open-framework gravels can act, during complete saturation, as preferential flow paths, and capillary barrier effects may occur under variably saturated conditions. These hypotheses were tested by measuring water content variations (using time domain reflectometry probes) at three depths (0, –0.5, and –1.15 m). Experimental data show infiltration behavior that can be explained by a capillary barrier effect between the two lower probes. These results suggest that our hypothesis about hydraulic behavior is reasonable
It is known that the heterogeneity of hydraulic conductivity drives the groundwater flow and the transport of contaminants. However, in conventional characterization methods, the lack of densely sampled hydrological data does not permit us to describe the aquifer heterogeneity at an appropriate scale. In this study, we integrate ground-penetrating radar (GPR) tomographic data with hydraulic conductivity logs to estimate the hydraulic conductivity of a heterogeneous unconsolidated aquifer at a decimetric scale between two boreholes. The integration of these different data sets is achieved using a nonlinear Bayesian simulation algorithm. The prior hydraulic conductivity distribution is estimated, under Gaussian hypothesis, by simple kriging of the hydraulic well data. The likelihood of hydraulic conductivity given the relative permittivity and the electrical conductivity functions is obtained from a kernel probability density function estimator that describes the in-situ relationship between the electric and the hydraulic properties measured along boreholes. The proposed method is tested on a synthetic heterogeneous model of permeability to validate the methodology. We show that permeability realizations obtained from the proposed algorithm present a higher correlation with the synthetic model than other classical simulation methods. The method is then applied on data acquired over an unconsolidated aquifer located in Saint-Lambert-de-Lauzon, Quebec, Canada. The data set consists of measurements from (i) GPR crosshole acquisition, (ii) cone penetration testing with pressure measurement combined with soil moisture resistivity, and (iii) a borehole electromagnetic flowmeter. By using the presented Bayesian approach, we generated multiple hydraulic conductivity realizations that are in good agreement with the hydrogeological model of the area.
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