Surface Ground Penetrating Radar Data Research Articles

Application of Subset Full-Waveform Inversion to Multioffset Surface Ground-Penetrating Radar Data

Application of Subset Full-Waveform Inversion to Multioffset Surface Ground-Penetrating Radar Data

Crosshole reflection imaging with ground-penetrating radar data: Applications in near-surface sedimentary settings

Crosshole ground-penetrating radar (GPR) is applied in areas that require a very detailed subsurface characterization. Analysis of such data typically relies on tomographic inversion approaches providing an image of subsurface parameters. We have developed an approach for processing the reflected energy in crosshole GPR data and applied it on GPR data acquired in different sedimentary settings. Our approach includes muting of the first arrivals, separating the up- and the downgoing wavefield components, and backpropagating the reflected energy by a generalized Kirchhoff migration scheme. We obtain a reflection image that contains information on the location of electromagnetic property contrasts, thus outlining subsurface architecture in the interborehole plane. In combination with velocity models derived from different tomographic approaches, these images allow for a more detailed interpretation of subsurface structures without the need to acquire additional field data. In particular, a combined interpretation of the reflection image and the tomographic velocity model improves the ability to locate layer boundaries and to distinguish different subsurface units. To support our interpretations of our field data examples, we compare our crosshole reflection results with independent information, including borehole logs and surface GPR data.

Measuring vertical soil water content profiles by combining horizontal borehole and dispersive surface ground penetrating radar data

ABSTRACTTo investigate transient dynamics of soil water redistribution during infiltration, we conducted horizontal borehole and surface ground penetrating radar measurements during a 4‐day infiltration experiment at the rhizontron facility in Selhausen, Germany. Zero‐offset ground penetrating radar profiling in horizontal boreholes was used to obtain soil water content information at specific depths (0.2, 0.4, 0.6, 0.8 and 1.2 m). However, horizontal borehole ground penetrating radar measurements do not provide accurate soil water content estimates of the top soil (0–0.1 m depth) because of interference between direct and critically refracted waves. Therefore, surface ground penetrating radar data were additionally acquired to estimate soil water content of the top soil. Due to the generation of electromagnetic waveguides in the top soil caused by infiltration, a strong dispersion in the ground penetrating radar data was observed in 500 MHz surface ground penetrating radar data. A dispersion inversion was thus performed with these surface ground penetrating radar data to obtain soil water content information for the top 0.1 m of the soil. By combining the complementary borehole and surface ground penetrating radar data, vertical soil water content profiles were obtained, which were used to investigate vertical soil water redistribution. Reasonable consistency was found between the ground penetrating radar results and independent soil water content data derived from time domain reflectometry measurements. Because of the improved spatial representativeness of the ground penetrating radar measurements, the soil water content profiles obtained by ground penetrating radar better matched the known water storage changes during the infiltration experiment. It was concluded that the combined use of borehole and surface ground penetrating radar data convincingly revealed spatiotemporal soil water content variation during infiltration. In addition, this setup allowed a better quantification of water storage, which is a prerequisite for future applications, where, for example, the soil hydraulic properties will be estimated from ground penetrating radar data.

Tomogram-based comparison of geostatistical models: Application to the Macrodispersion Experiment (MADE) site

We propose a new framework to compare alternative geostatistical descriptions of a given site. Multiple realizations of each of the considered geostatistical models and their corresponding tomograms (based on inversion of noise-contaminated simulated data) are used as a multivariate training image. The training image is scanned with a direct sampling algorithm to obtain conditional realizations of hydraulic conductivity that are not only in agreement with the geostatistical model, but also honor the spatially varying resolution of the site-specific tomogram. Model comparison is based on the quality of the simulated geophysical data from the ensemble of conditional realizations. The tomogram in this study is obtained by inversion of cross-hole ground-penetrating radar (GPR) first-arrival travel time data acquired at the MAcro-Dispersion Experiment (MADE) site in Mississippi (USA). Various heterogeneity descriptions ranging from multi-Gaussian fields to fields with complex multiple-point statistics inferred from outcrops are considered. Under the assumption that the relationship between porosity and hydraulic conductivity inferred from local measurements is valid, we find that conditioned multi-Gaussian realizations and derivatives thereof can explain the crosshole geophysical data. A training image based on an aquifer analog from Germany was found to be in better agreement with the geophysical data than the one based on the local outcrop, which appears to under-represent high hydraulic conductivity zones. These findings are only based on the information content in a single resolution-limited tomogram and extending the analysis to tracer or higher resolution surface GPR data might lead to different conclusions (e.g., that discrete facies boundaries are necessary).

Combination of H-Alpha Decomposition and Migration for Enhancing Subsurface Target Classification of GPR

Polarimetric technology has been one of the most important advances in microwave remote sensing during recent decades. H-alpha decomposition, which is a type of polarimetric analysis technique, has been common for terrain and land-use classification in polarimetric synthetic aperture radar. However, the technique has been less common in the ground penetrating radar (GPR) community. In this paper, we apply the H-alpha decomposition to analyze the surface GPR data to obtain polarimetric attributes for subsurface target classification. Also, by combining H-alpha decomposition and migration, we can obtain a subsurface H-alpha color-coded reconstructed target image, from which we can use both the polarimetric attributes and the geometrical features of the subsurface targets to enhance the ability of subsurface target classification of surface GPR. A 3-D full polarimetric GPR data set was acquired in a laboratory experiment, in which four targets, a scatterer with many branches, a ball, a plate, and a dihedral scatter, were buried in dry sand under flat ground surface, and used to test these techniques. As results, we obtained the subsurface H-alpha distribution and classified the subsurface targets. Also, we derived a subsurface H-alpha color-coded reconstructed target image and identified all four targets in the laboratory experiment.

Coupled hydrogeophysical inversion of time-lapse surface GPR data to estimate hydraulic properties of a layered subsurface

[1] A major challenge in vadose zone hydrology is to obtain accurate information on the temporal changes of the vertical soil water distribution and its feedback with the atmosphere and groundwater. A state of the art coupled hydrogeophysical inversion scheme is applied to evaluate soil hydraulic properties of a synthetic model and a field soil in southern Ontario based on time-lapse monitoring of soil dynamics with surface ground-penetrating radar (GPR). Film flow was included in the hydrological model to account for noncapillary water flow in a sandy medium during dry conditions. The synthetic study illustrated that GPR data contain sufficient information to accurately constrain soil hydraulic parameters within a coupled inversion framework and led to an accurate estimation of the soil hydraulic properties. When film flow was not accounted for within the inversion, an equally good fit could still be achieved. In this case, errors introduced by neglecting film flow were compensated by different hydraulic parameters. For the field data, the coupled inversion reduced the overall misfit compared to an uncalibrated model using hydraulic parameters obtained from laboratory data. Although the data fit improved significantly for water content in the deeper soil layers, accounting for film flow in the uppermost subsurface layer did not lead to a better fit of the GPR data. Further research is needed to describe the processes controlling water content in the dry range, in particular coupled heat and vapor transport. This study illustrates the suitability of surface GPR measurements combined with coupled inversion for near-surface characterization of soil hydraulic parameters.

Quantitative analysis of water-content estimation errors using ground-penetrating radar data and a low-loss approximation

Expressions are derived to quantify the error when estimating permittivity that results from using the low-loss approximation under lossy conditions and to examine the repercussions on estimating water content [Formula: see text]. Values are computed under a range of porosity, clay-content, water-quality, and frequency conditions. Although in most cases the error is negligible, it can be significant for some hydrogeophysical applications involving cross-hole measurements or low-frequency surface ground-penetrating radar (GPR). For instance, when the loss tangent [Formula: see text] equals 0.5, corresponding to an effective conductivity of [Formula: see text], a dielectric constantof 11, and a frequency of [Formula: see text], the relative error on dielectric permittivity is approximately 6%. If the conductivity doubles or the frequency is halved, the loss tangentdoubles but the error grows to 21%. In addition, considering a situation where the porosity is 20% and [Formula: see text], the use of the low-loss approximation leads to a 10% deviation from [Formula: see text]. In the context of water-content estimation, we therefore suggest to perform attenuation tomography, in addition to velocity tomography for crosshole data, or estimate the quality factor [Formula: see text] for surface GPR data to compute the loss tangent over the probed area. If proven necessary, the parameters sought can then be determined more accurately using a lossy formulation. We also propose to supplement GPR measurements with electrical-resistivity tomography to constrain the borehole GPR amplitude data-processing steps required by attenuation tomography or to complement the characterization of the survey area and improve the knowledge brought by [Formula: see text] estimates alone.

Image enhancement with wave-equation redatuming: application to GPR data collected at public transportation sites

In this paper, we describe the application of wave-equation redatuming through downward and upward continuations of surface ground penetrating radar (GPR) data to image the target beneath a surface layer with geometric or material property heterogeneities. We first tested this technique with the use of a synthetic radar profile generated by the finite difference time domain method to show its effectiveness. We then applied it to the surface GPR data collected in several urban public transportation infrastructure sites including a project to use GPR to assess the quality of mortar consolidation behind a tunnel wall in the Xiangyin highway tunnel, Shanghai, China. The purpose of this redatuming processing is to enhance target images in later times of a GPR profile, by eliminating the contamination in the GPR profile caused by strong diffractive scattering from the steel rebars in the surface concrete slab or tunnel wall. The results for synthetic and observed GPR data show that the wave-equation redatuming technique is an effective way to eliminate unwanted diffraction signatures and ease GPR image interpretation for data collected at a site with a strongly heterogeneous surface layer.

Estimation of the spatial distribution of fluid permeability from surface and tomographic GPR data and core, with a 2‐D example from the Ferron Sandstone, Utah

Reservoir analogs provide detailed information that is applicable to fluid transport simulations but that cannot be obtained directly from reservoirs because of inaccessibility. The Ferron Sandstone of east‐central Utah is an analog for fluviodeltaic reservoirs; its excellent outcrop exposures are ideal for detailed study. Ground‐penetrating radar (GPR) data were collected in and between two cored boreholes and are used to build a 2‐D fluid permeability model in four steps. First, an anisotropic GPR propagation velocity model is obtained from traveltime tomography between two boreholes and between each borehole and the earth's surface. Second, the geometry of the sedimentological features is imaged by prestack Kirchhoff depth migration of constant‐offset GPR data acquired along a line between the two holes at the earth's surface. Third, a background permeability is assigned to each layer by interpolating the geometrical average of the measured permeabilities in each sedimentological element. Finally, the spatial distribution of flow baffles and barriers is estimated by calibrating the instantaneous amplitude and frequency of the surface GPR data associated with the mudstone layers in the boreholes via cluster analysis. The result is an integrated model that contains GPR velocity, lithology, and fluid permeability distributions. Low GPR velocities correspond to mudstones with low permeability. The main mudstone layers (potential barriers and/or baffles to fluid flow) do not appear to be continuous between the boreholes, which means that interpretations based on borehole data alone would overestimate element continuity and thereby underestimate effective permeability.