Geostatistical inversion of seismic and ground‐penetrating radar reflection images: What can we actually resolve?

Irving, James; Holliger, Klaus

doi:10.1029/2010gl044852

Cited by 20 publications

(12 citation statements)

References 17 publications

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“…They found that unique recovery of the lateral correlation structure is dependent upon accurate knowledge of the vertical correlation structure. This finding was subsequently demonstrated mathematically by Irving and Holliger (). The developed inversion methodology was successfully applied to both synthetic and field GPR measurements, as well as to synthetic seismic reflection data (Irving et al .…”

Section: Introductionmentioning

confidence: 56%

“…Irving et al . , ; Irving and Holliger ), the 3D inversion cannot constrain uniquely the horizontal correlation lengths, but rather only the horizontal‐to‐vertical aspect ratios of the underlying heterogeneity. As a result, we present our results in terms of the aspect ratios

a_{x^{'}} / a_{z^{'}}

and

a_{y^{'}} / a_{z^{'}}

, along with the lateral aspect ratio

a_{y^{'}} / a_{x^{'}}

.…”

Section: Application To Field Datamentioning

confidence: 99%

“…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 ). Rea and Knight () compared the correlation structure of an outcrop image with that of the corresponding GPR data and found good overall agreement.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 56%

a_{x^{'}} / a_{z^{'}}

and

a_{y^{'}} / a_{z^{'}}

, along with the lateral aspect ratio

a_{y^{'}} / a_{x^{'}}

.…”

Section: Application To Field Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…GPR sections exhibit vertical as well as lateral correlation (e.g., [55]), which are due to the frequency-bandlimited nature of GPR data and the spatial correlation (spatial bandlimited nature) of the underlying reflectivity. We assessed the influence of temporal and spatial correlation by computing the optimum phase angle and kurtosis for the bandlimited reflectivity model used before but selected the traces randomly.…”

Section: Discussionmentioning

confidence: 99%

Efficient Deconvolution of Ground-Penetrating Radar Data

Schmelzbach¹,

Huber

2015

IEEE Trans. Geosci. Remote Sensing

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The time (vertical) resolution enhancement of ground-penetrating radar (GPR) data by deconvolution is a longstanding problem due to the mixed-phase characteristics of the source wavelet. Several approaches have been proposed, which take the mixed-phase nature of the GPR source wavelet into account. However, most of these schemes are usually laborious and/or computationally intensive and have not yet found widespread use. Here, we propose a simple and fast approach to GPR deconvolution that requires only a minimal user input. First, a trace-by-trace minimum-phase (spiking) deconvolution is applied to remove the minimum-phase part of the mixed-phase GPR wavelet. Then, a global phase rotation is applied to maximize the sparseness (kurtosis) of the minimum-phase deconvolved data to correct for phase distortions that remain after the minimum-phase deconvolution. Applications of this scheme to synthetic and field data demonstrate that a significant improvement in image quality can be achieved, leading to deconvolved data that are a closer representation of the underlying reflectivity structure than the input or minimum-phase deconvolved data. Synthetic-data tests indicate that, because of the temporal and spatial correlation inherent in the GPR data due to the frequency-and wavenumber-bandlimited nature of the GPR source wavelet and the reflectivity structure, a significant number of samples are required for a reliable sparseness (kurtosis) estimate and stable phase rotation. This observation calls into question the blithe application of kurtosis-based methods within short time windows such as that for time-variant deconvolution.

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“…The boundaries between the large-scale units are expected to show larger reflection coefficients than those of the small-scale variations (e.g., Holliger, 1996b;Walden and Hosken, 1986). Considering the universally scale invariant behavior of many subsurface material parameters (e.g., Turcotte, 1997), it is reasonable to assume that this model also applies to the subsurface electrical properties (e.g., Irving and Holliger, 2010).…”

Section: Realistic 1-d Reflectivity Models (Models B and C)mentioning

confidence: 98%