Bayesian frequency-domain blind deconvolution of ground-penetrating radar data

Schmelzbach, Cédric; Scherbaum, Frank; Tronicke, Jens; Dietrich, Peter

doi:10.1016/j.jappgeo.2011.08.010

Cited by 15 publications

(7 citation statements)

References 72 publications

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“…GPR deconvolution [ Schmelzbach et al , 2011a] is key to remove the distortions of the recorded reflection data due to the shape of the mixed‐phase source wavelet. Standard deconvolution routines either involve the estimation of the source wavelet from the data assuming it is minimum phase, which is often not met by GPR data, or require that the source wavelet is known, which is very difficult for field data.…”

Section: Methodsmentioning

confidence: 99%

“…Because of the negative sign of the maximum source wavelet amplitudes, reflections in the GPR data trace show the opposite sign than the corresponding spikes in the reflectivity. GPR deconvolution [ Schmelzbach et al , 2011a] was used to remove the mixed‐phase source wavelet (Figure 3a) and broaden the data amplitude spectrum within the signal bandwidth of ∼40–140 MHz. Note the significantly improved similarity between the deconvolved data (Figure 2d) and the reflectivity (Figure 2b).…”

Section: Synthetic Examplementioning

confidence: 99%

“…Because of the mixed‐phase GPR source wavelet, standard deconvolution routines are often applied to GPR data with only limited success [see, e.g., Belina et al , 2009a]. In contrast, the recently proposed GPR deconvolution routine by Schmelzbach et al [2011a] offers a robust way to estimate and remove the source wavelet from GPR data.…”

Section: Introductionmentioning

confidence: 99%

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High‐resolution water content estimation from surface‐based ground‐penetrating radar reflection data by impedance inversion

Schmelzbach

Tronicke

Dietrich

2012

Water Resources Research

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1] Mapping hydrological parameter distributions in high resolution is essential to understand and simulate groundwater flow and contaminant transport. Of particular interest is surface-based ground-penetrating radar (GPR) reflection imaging in electrically resistive sediments because of the expected close link between the subsurface water content and the dielectric permittivity, which controls GPR wave velocity and reflectivity. Conventional tools like common midpoint (CMP) velocity analysis provide physical parameter models of limited resolution only. We present a novel reflection amplitude inversion workflow for surface-based GPR data capable of resolving the subsurface dielectric permittivity and related water content distribution with markedly improved resolution. Our scheme is an adaptation of a seismic reflection impedance inversion scheme to surface-based GPR data. Key is relative-amplitude-preserving data preconditioning including GPR deconvolution, which results in traces with the source-wavelet distortions and propagation effects largely removed. The subsequent inversion for the underlying dielectric permittivity and water content structure is constrained by in situ dielectric permittivity data obtained by direct-push logging. After demonstrating the potential of our novel scheme on a realistic synthetic data set, we apply it to two 2-D 100 MHz GPR profiles acquired over a shallow sedimentary aquifer resulting in water content images of the shallow (3-7 m depth) saturated zone having decimeter resolution.Citation: Schmelzbach, C., J. Tronicke, and P. Dietrich (2012), High-resolution water content estimation from surface-based ground-penetrating radar reflection data by impedance inversion, Water Resour. Res., 48, W08505,

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Section: Methodsmentioning

confidence: 99%

Section: Synthetic Examplementioning

confidence: 99%

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High‐resolution water content estimation from surface‐based ground‐penetrating radar reflection data by impedance inversion

Schmelzbach

Tronicke

Dietrich

2012

Water Resources Research

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“…The canonical representation of a wavelet [ (2) and (3)] has been used to estimate mixed-phase wavelets [33], [45]- [48]. These schemes differ in the way the individual components are found, but all use computationally expensive schemes to find the mixed-phase wavelet.…”

Section: B Implementationmentioning

confidence: 99%

“…A reliable estimation of the kurtosis requires a large number of samples (e.g., [33] and [38]). We therefore estimate one global residual phase correction for all traces within the signal window (50-300 ns).…”

Section: Realistic 2-d Synthetic Data Examplementioning

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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