The effect of vertical measurement resolution on the correlation structure of a ground penetrating radar reflection image

Knight, Rosemary; Tercier, Paulette; Irving, James

doi:10.1029/2004gl021112

Cited by 6 publications

(11 citation statements)

References 11 publications

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“…However, as in the case of our synthetic example, we see that the higher-frequency data appear to have a shorter lateral correlation length. This is because reflectors which effectively ''line up'' when imaged at 100 MHz become horizontally discontinuous when imaged at 200 MHz, again confirming the dependence on vertical resolution of the horizontal correlation structure of the GPR data [Knight et al, 2004]. This effective decrease in correlation length of the data with increasing frequency is better seen in Figure 9, which shows the calculated 2-D autocorrelations of the 100 and 200 MHz data along with horizontal and vertical slices through the centers of these autocorrelations.…”

Section: Field Data Examplesupporting

confidence: 52%

Estimation of the lateral correlation structure of subsurface water content from surface‐based ground‐penetrating radar reflection images

Irving

Knight

Holliger

2009

Water Resources Research

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[1] Over the past decade, significant interest has been expressed in relating the spatial statistics of surface-based reflection ground-penetrating radar (GPR) data to those of the imaged subsurface volume. A primary motivation for this work is that changes in the radar wave velocity, which largely control the character of the observed data, are expected to be related to corresponding changes in subsurface water content. Although previous work has indeed indicated that the spatial statistics of GPR images are linked to those of the water content distribution of the probed region, a viable method for quantitatively analyzing the GPR data and solving the corresponding inverse problem has not yet been presented. Here we address this issue by first deriving a relationship between the 2-D autocorrelation of a water content distribution and that of the corresponding GPR reflection image. We then show how a Bayesian inversion strategy based on Markov chain Monte Carlo sampling can be used to estimate the posterior distribution of subsurface correlation model parameters that are consistent with the GPR data. Our results indicate that if the underlying assumptions are valid and we possess adequate prior knowledge regarding the water content distribution, in particular its vertical variability, this methodology allows not only for the reliable recovery of lateral correlation model parameters but also for estimates of parameter uncertainties. In the case where prior knowledge regarding the vertical variability of water content is not available, the results show that the methodology still reliably recovers the aspect ratio of the heterogeneity.Citation: Irving, J., R. Knight, and K. Holliger (2009), Estimation of the lateral correlation structure of subsurface water content from surface-based ground-penetrating radar reflection images, Water Resour. Res., 45, W12404,

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Section: Field Data Examplesupporting

confidence: 52%

Estimation of the lateral correlation structure of subsurface water content from surface‐based ground‐penetrating radar reflection images

Irving

Knight

Holliger

2009

Water Resources Research

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“…The interaction of the radar signal with this mixture is affected both by the volumetric ratio of ice to air as well as by the shape and orientation of the ice crystals and air voids. However, Harper and Bradford [2003] show that, for cold snow, the complex refractive index model (CRIM) equation [ Wharton et al , 1980; Knight et al , 2004] can be adapted to closely estimate the bulk density of the mixture based on the velocity of the EM wave propagation. The adapted CRIM equation is

ρ_{f} = (\frac{\frac{true}{v_{a}} - 1}{\frac{true}{v_{a}} - 1}) ρ_{i},

where ρ f and ρ i are the densities of firn and ice, respectively, and ν f , ν i , and ν a are the EM propagation velocities of firn, ice, and air, respectively.…”

Section: Methodsmentioning

confidence: 99%

Georadar‐derived estimates of firn density in the percolation zone, western Greenland ice sheet

et al. 2012

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[1] Greater understanding of variations in firn densification is needed to distinguish between dynamic and melt-driven elevation changes on the Greenland ice sheet. This is especially true in Greenland's percolation zone, where firn density profiles are poorly documented because few ice cores are extracted in regions with surface melt. We used georadar to investigate firn density variations with depth along a $70 km transect through a portion of the accumulation area in western Greenland that partially melts. We estimated electromagnetic wave velocity by inverting reflection traveltimes picked from common midpoint gathers. We followed a procedure designed to find the simplest velocity versus depth model that describes the data within estimated uncertainty. On the basis of the velocities, we estimated 13 depth-density profiles of the upper 80 m using a petrophysical model based on the complex refractive index method equation. At the highest elevation site, our density profile is consistent with nearby core data acquired in the same year. Our profiles at the six highest elevation sites match an empirically based densification model for dry firn, indicating relatively minor amounts of water infiltration and densification by melt and refreeze in this higher region of the percolation zone. At the four lowest elevation sites our profiles reach ice densities at substantially shallower depths, implying considerable meltwater infiltration and ice layer development in this lower region of the percolation zone. The separation between these two regions is 8 km and spans 60 m of elevation, which suggests that the balance between dry-firn and melt-induced densification processes is sensitive to minor changes in melt.

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“…() observed similarities between the horizontal correlation statistics of GPR reflection data and those of closely spaced neutron‐probe water‐content measurements, but pointed to the results of previous work demonstrating that the lateral correlation structure of a GPR reflection image will be strongly influenced by the vertical measurement resolution, which in turn is controlled by the antenna centre frequency (Knight et al . ).…”

Section: Introductionmentioning

confidence: 97%

“…To date, several attempts have been made to relate the lateral correlation statistics of surface‐based reflection GPR data to those of the investigated subsurface region (e.g. Rea and Knight ; Oldenborger, Knoll and Barrash ; Knight, Tercier and Irving ; Dafflon, Tronicke and Holliger ; Knight et al . ; Irving, Knight and Holliger ; Irving and Holliger ; Irving, Scholer and Holliger ).…”

Section: Introductionmentioning

confidence: 99%

Estimation of the 3D correlation structure of an alluvial aquifer from surface‐based multi‐frequency ground‐penetrating radar reflection data

Irving

Lindsay

et al. 2019

Geophysical Prospecting

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Knowledge about the stochastic nature of heterogeneity in subsurface hydraulic properties is critical for aquifer characterization and the corresponding prediction of groundwater flow and contaminant transport. Whereas the vertical correlation structure of the heterogeneity is often well constrained by borehole information, the lateral correlation structure is generally unknown because the spacing between boreholes is too large to allow for its meaningful inference. There is, however, evidence to suggest that information on the lateral correlation structure may be extracted from the correlation statistics of the subsurface reflectivity structure imaged by surface‐based ground‐penetrating radar measurements. To date, case studies involving this approach have been limited to 2D profiles acquired at a single antenna centre frequency in areas with limited complementary information. As a result, the practical reliability of this methodology has been difficult to assess. Here, we extend previous work to 3D and consider reflection ground‐penetrating radar data acquired using two antenna centre frequencies at the extensively explored and well‐constrained Boise Hydrogeophysical Research Site. We find that the results obtained using the two ground‐penetrating radar frequencies are consistent with each other, as well as with information from a number of other studies at the Boise Hydrogeophysical Research Site. In addition, contrary to previous 2D work, our results indicate that the surface‐based reflection ground‐penetrating radar data are not only sensitive to the aspect ratio of the underlying heterogeneity, but also, albeit to a lesser extent, to the so‐called Hurst number, which is a key parameter characterizing the local variability of the fine‐scale structure.

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The effect of vertical measurement resolution on the correlation structure of a ground penetrating radar reflection image

Cited by 6 publications

References 11 publications

Estimation of the lateral correlation structure of subsurface water content from surface‐based ground‐penetrating radar reflection images

Estimation of the lateral correlation structure of subsurface water content from surface‐based ground‐penetrating radar reflection images

Georadar‐derived estimates of firn density in the percolation zone, western Greenland ice sheet

Estimation of the 3D correlation structure of an alluvial aquifer from surface‐based multi‐frequency ground‐penetrating radar reflection data

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