Enhancing the vertical resolution of surface georadar data

Belina, F.A.; Dafflon, Baptiste; Tronicke, Jens; Holliger, Klaus

doi:10.1016/j.jappgeo.2008.08.011

Cited by 38 publications

(24 citation statements)

References 24 publications

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“…Deconvolution, the common remedy to this problem in seismics, has been shown to be more difficult for GPR data (e.g., Turner, 1994;Irving and Knight, 2003). Two key reasons for this difficulty are dispersion of the emitted waveform and its non-minimum-phase character (Belina et al, 2009). The former causes changes in the shape of the radar wave as it travels through the medium, which makes the task of removing one specific waveform inaccurate.…”

Section: Gpr Data Processing Methodologymentioning

confidence: 99%

“…This can lead to non-convergent deconvolution operators. Recently, Xia et al (2004) and Belina et al (2009) successfully tested deconvolution techniques for GPR recordings on low-dispersion soils. Additionally, Spikes et al (2004) used a "spiking deconvolution in RADAN" (S. Arcone, personal communication) for simplifying firn radar profiles.…”

Section: Gpr Data Processing Methodologymentioning

confidence: 99%

“…Radar has been utilised for glaciological analysis of ice thickness and the detection of internal layers for many years, e.g., Bentley et al (1979), Bogorodsky et al (1985), Arcone et al (1995), Eisen et al (2003), and Rotschky et al (2006). Reflections seen in radar recordings can sometimes be associated with distinct accumulation or melt events, or depth hoar layers (Eisen et al, 2004;Arcone et al, 2005;Helm et al, 2007;Dunse et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Snow accumulation and compaction derived from GPR data near Ross Island, Antarctica

et al. 2011

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Abstract. We present an improved method for estimating accumulation and compaction rates of dry snow in Antarctica with ground penetrating radar (GPR). Using an estimate of the emitted waveform from direct measurements, we apply deterministic deconvolution via the Fourier domain to GPR data with a nominal frequency of 500 MHz. This reveals unambiguous reflection horizons which can be observed in repeat measurements made one year apart. At two measurement sites near Scott Base, Antarctica, we extrapolate point measurements of average accumulation from snow pits and firn cores to a larger area by identifying a dateable dust layer horizon in the radargrams. Over an 800 m × 800 m area on the McMurdo Ice Shelf (77 • 45 S, 167 • 17 E) the average accumulation is found to be 269 ± 9 kg m −2 a −1 . The accumulation over an area of 400 m× 400 m on Ross Island (77 • 40 S, 167 • 11 E, 350 m a.s.l.) is found to be higher (404 ± 22 kg m −2 a −1 ) and shows increased variability related to undulating terrain. Compaction of snow between 2 m and 13 m depth is estimated at both sites by tracking several internal reflection horizons along the radar profiles and calculating the average change in separation of horizon pairs from one year to the next. The derived compaction rates range from 7 cm m −1 at a depth of 2 m, down to no measurable compaction at 13 m depth, and are similar to published values from point measurements.

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Section: Gpr Data Processing Methodologymentioning

confidence: 99%

Section: Gpr Data Processing Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Snow accumulation and compaction derived from GPR data near Ross Island, Antarctica

et al. 2011

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“…For data interpretation, spectral decomposition of GPR data offers the possibility to characterize the subsurface architecture based on analyzing the frequency evolution with time and depth (Bradford and Wu, 2007;Geerdes and Young, 2007). Additionally, time-frequency analysis allows for advanced data processing, e.g., time-varying frequency filtering, inverse Q-filtering, or spectral balancing to enhance the resolution of GPR data (Belina et al, 2009;Economou and Vafidis, 2010;Irving and Knight, 2003). The quality and reliability of such analyses and processing steps depend strongly on the resolution of the selected time-frequency representation.…”

Section: Introductionmentioning

confidence: 99%

High-resolution GPR data analysis using extended tree-based pursuit

Böniger

Tronicke

2012

Journal of Applied Geophysics

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“…A more effective thinking is to decompose the data into signal and noise components, followed by removing the noise and saving the signal (Jeng et al, 2007a(Jeng et al, , 2009Lin and Jeng, 2010). This idea has been implemented by using a number of regular Fourier-based linear filtering algorithms, but most of the methods result in serious mixmode problem and an abundance of spurious harmonics without any physical meaning (Jeng, 1995;Jeng et al, 2007b;Belina et al, 2009). This study is to introduce a data processing scheme by employing a new nonlinear data processing method, EEMD (ensemble empirical mode decomposition), with the technique of logarithmic transform to recover the GPR data signal.…”

Section: Introductionmentioning

confidence: 99%