Analysis of multi‐offset GPR data: a case study in a coarse‐grained gravel aquifer

Becht, Andreas; Appel, Erwin; Dietrich, P.

doi:10.3997/1873-0604.2005047

Cited by 18 publications

(21 citation statements)

References 17 publications

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“…The accuracy and precision of physical properties therefore depend inherently on those of initial stacking quantities and, without numerous corrections, velocity models are unsuitable for quantitative interpretation and should only be used for data processing (e.g., the process of horizontal stacking, for which they are named; Sheriff and Geldart ; Yilmaz ; Becht et al . ).…”

Section: Terminology In Cmp‐based Velocity Analysismentioning

confidence: 97%

Influences on the resolution of GPR velocity analyses and a Monte Carlo simulation for establishing velocity precision

Booth

Clark

Murray

2011

Near Surface Geophysics

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The precision of ground‐penetrating radar velocity models is seldom reported, despite their common use for quantifying subsurface properties. We explore influences on the resolution of ground‐penetrating radar velocity analysis and demonstrate a Monte Carlo method for obtaining the implied precision in velocity estimates. A series of synthetic common‐midpoint gathers, which assume Ricker wavelets of 50 MHz, 100 MHz and 200 MHz frequencies as source pulses, simulate hyperbolic reflections from the bases of three horizontal layers, each with interval velocity and thickness of 0.1 m/ns and 2 m, respectively. With these, we use coherence analysis to show that the principal influence on stacking velocity (vST) resolution is the traveltime moveout, normalized by the wavelet period, exhibited across some offset range. Where moveout exceeds the period by factors of, e.g., 3 and 6, vST is resolved to [–6.5, +8.2]% to [–3.6, +4.0]%, respectively, at the 50% coherence threshold. The temporal duration of coherence responses is expressed as a one‐quarter wavelet period either side of the stacking traveltime. Coherence resolutions are used in Monte Carlo simulations to derive the implied precision in the interval stacking velocity (vIS) and its derivative properties. These analyses are repeated for field common‐midpoint data, acquired using 50 MHz antennas, on Quaternary glacio‐fluvial media overlying a Cambrian basement (<15 m deep). For three reflections identified in these data, the velocity and temporal resolution of coherence responses vary in a quantitatively similar way to events in the synthetic data. For the corresponding layers, Monte Carlo simulations yield precision estimates for vIS, layer thickness and fractional water content. The procedure predicts a reasonable model of water content decreasing with depth, in accordance with increasing pore compaction and provides a robust error analysis; the uncertainty in estimates of the fractional water content of two (assumed) water‐saturated layers is ±2.6% and ±2.1% (shallower and deeper, respectively). Monte Carlo simulations are recommended as an efficient means of establishing the precision in quantitative estimates of subsurface properties derived from ground‐penetrating radar velocities, particularly where ground‐truth data are unavailable.

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Section: Terminology In Cmp‐based Velocity Analysismentioning

confidence: 97%

Influences on the resolution of GPR velocity analyses and a Monte Carlo simulation for establishing velocity precision

Booth

Clark

Murray

2011

Near Surface Geophysics

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“…As previously noted, the level of uncertainty in the interval velocity‐depth model obtained from the NMO velocity analysis of an individual CMP sounding is sensitive to numerous factors. In particular, these results are strongly dependent on the magnitude of the vertical EM wave velocity gradients present in the subsurface [ Becht et al , 2006]. Given the highly variable nature of water content in the shallow vadose zone, one would expect significant variations in vertical velocity gradients over the annual cycle of hydrological conditions.…”

Section: Experimental Descriptionmentioning

confidence: 99%

High‐resolution ground‐penetrating radar monitoring of soil moisture dynamics: Field results, interpretation, and comparison with unsaturated flow model

Steelman

Endres

Jones

2012

Water Resources Research

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[1] Surface ground-penetrating radar (GPR) techniques have been used by a number of previous researchers to characterize soil moisture content in the vadose zone. However, limited temporal sampling and low resolution near the surface in these studies greatly impedes the quantitative analysis of vertical soil moisture distribution and its associated dynamics within the shallow subsurface. To further examine the capacity of surface GPR, we have undertaken an extensive 26 month field study using concurrent high-frequency (i.e., 900 MHz) reflection profiling and common-midpoint (CMP) soundings to quantitatively monitor soil moisture distribution and dynamics within the shallow vadose zone. This unprecedented data set allowed us to assess the concurrent use of these techniques over two contrasting annual cycles of soil conditions. Reflection profiles provided high-resolution traveltime data between four stratigraphic reflection events while cumulative results of the CMP sounding data set produced precise depth estimates for those reflecting interfaces, which were used to convert interval-traveltime data into soil moisture. The downward propagation of major infiltration episodes associated with seasonal and transient events are well resolved by the GPR data. The use of CMP soundings permitted the determination of direct ground wave velocities, which provided high-resolution information along the air-soil interface. This improved resolution enabled better characterization of short-duration wetting/drying and freezing/thawing processes, and permitted better evaluation of the nature of the coupling between shallow and deep moisture conditions. The nature of transient infiltration pulses, evapotranspiration episodes, and deep drainage patterns observed in the GPR data series were further examined by comparing them with a vertical soil moisture flow simulation based on the variably saturated model, HYDRUS-1D. Using laboratory-derived soil hydraulic property information from soil samples and a number of simplifying assumptions about the upper and lower-boundary condition, we were able to achieve very good agreement between measured and simulated soil moisture profiles without model calibration; this is a strong indication of the overall quality of the GPR-derived soil moisture estimates. The only notable difference between simulated values and GPR water content estimates occurred during extended dry soil conditions near the surface.

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“…The match between

v_{S T}

and

v_{R M S}

may be particularly poor where the short‐spread approximation is violated, for anisotropic layering and/or where there is significant refraction across velocity interfaces; Becht et al . () showed that this effect is particularly marked where velocity decreases with depth. For a reflector with dip angle

α

, the cosine of

α

is included in the denominator of equation (), hence

v_{S T}

is always biased faster than

v_{R M S}

(Levin ; Deregowski ).…”

Section: Velocity Analysis Of Cmp Gathersmentioning

confidence: 92%

Semblance response to a ground‐penetrating radar wavelet and resulting errors in velocity analysis

Booth

Clark

Murray

2010

Near Surface Geophysics

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The propagation velocity of a ground‐penetrating radar (GPR) wavelet may be used to derive physical subsurface properties, including layer thickness, porosity and water content. We describe a systematic error in semblance analysis of GPR common‐midpoint (CMP) data, arising from the response of the statistic to the waveform of the GPR pulse. Only the first‐breaks of GPR wavelets express true velocities and traveltimes but these cannot deliver a semblance response since they have zero amplitude; instead, this response derives from subsequent wavelet half‐cycles, delayed from the first‐break. This delay causes semblance picks to express slower velocity and later traveltime with respect to true quantities, even for simple cases of reflectivity. For a two‐layer synthetic CMP data set, in which the GPR source pulse via a 500 MHz Berlage wavelet, semblance picks underestimate interval velocities of 0.135 m/ns and 0.060 m/ns by 10.0% and 2.0%, respectively. Velocity biases are corrected using the coherence statistic to simulate first‐break traveltimes from a set of velocity picks, in a process termed ‘backshifting’. A t2−x2 linear regression of simulated first‐breaks yields smaller errors in the same interval velocities of −2.2% and −1.0%. A first real‐data example considers a reflection from the base of a 3.39 m thick air‐gap. Semblance analysis estimates the air‐wave velocity as 0.289 m/ns (−3.6% error) and the air‐gap thickness as 3.59 m (+6.1% error); backshifting yields equivalent estimates of 0.302 m/ns (+0.9% error) and 3.35 m (−1.2% error). In a second example, semblance‐ and backshifting‐derived velocity models overestimate the thickness of clay‐rich archaeological deposits by 19.0% and 3.1%, respectively. Backshifting is a simple modification to conventional practice and is recommended for any analysis where physical subsurface properties are to be derived from the output GPR velocity.

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Analysis of multi‐offset GPR data: a case study in a coarse‐grained gravel aquifer

Cited by 18 publications

References 17 publications

Influences on the resolution of GPR velocity analyses and a Monte Carlo simulation for establishing velocity precision

Influences on the resolution of GPR velocity analyses and a Monte Carlo simulation for establishing velocity precision

High‐resolution ground‐penetrating radar monitoring of soil moisture dynamics: Field results, interpretation, and comparison with unsaturated flow model

Semblance response to a ground‐penetrating radar wavelet and resulting errors in velocity analysis

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