Synthetic GPR modelling studies on shallow geological properties and its comparison with the real data

Öztürk, Caner; Drahor, Mahmut G.

doi:10.1109/icgpr.2010.5550215

Cited by 2 publications

(2 citation statements)

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“…The role of GPR modelling is essential in advancing the GPR interpretation as it can provide additional information on targets by accurately reproducing the response of subsurface materials to electromagnetic (EM) waves. By simulating the propagation of these waves through different types of subsurface materials with various geometries, numerical modelling can reproduce how EM waves interact with the expected subsurface features, such as buried objects (Diamanti & Annan, 2019; González‐Huici & Giovanneschi, 2013; Kelly et al., 2021), geological structures (Giannopoulos & Diamanti, 2008; Öztürk & Drahor, 2010), water content variations (Bano, 2006), as well as a glaciers’ internal structure (Hunziker et al., 2023; Moran et al., 2003). This information can further be used to discriminate between real subsurface features and coherent noise or artefacts that can hinder interpretation.…”

Section: Introductionmentioning

confidence: 99%

GPR modelling and inversion to quantify the debris content within ice

Santin,

Roncoroni,

Forte

et al. 2023

Near Surface Geophysics

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Scattering is often detected when ground‐penetrating radar (GPR) surveys are performed on glaciers at different latitudes and in various environments. This event is often seen as an undesirable feature on data, but it can be exploited to quantify the debris content in mountain glaciers through a dedicated scattering inversion approach. At first, we considered the possible variables affecting the scattering mechanisms, namely the dielectric properties of the scatterers, their size, shape and quantity, as well as the wavelength of the electromagnetic (EM) incident field to define the initial conditions for the inversion. Each parameter was independently evaluated with forward modelling tests to quantify its effect in the scattering mechanism. After extensive tests, we found that the dimension and the amount of scatterers are the crucial parameters. We further performed modelling randomizing the scatterer distribution and dimension, critically evaluating the stability of the approach and the complexity of the models. After the tests on synthetic data, the inversion procedure was applied to field datasets, acquired on the Eastern Gran Zebrù glacier (Central Italian Alps). The results show that even a low percentage of debris can produce high scattering. The proposed methodology is quite robust and able to provide quantitative estimates of the debris content within mountain glaciers in different conditions.

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Section: Introductionmentioning

confidence: 99%

GPR modelling and inversion to quantify the debris content within ice

Santin,

Roncoroni,

Forte

et al. 2023

Near Surface Geophysics

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“…Numerical modeling of GPR is considered to be an alternative interpretation approach [17] and has been extensively applied to a number of GPR applications, among them are: the detection of dense nonaqueous phase liquids (DNAPL) [20], [21], the detection of geological targets like faults and caves [22], [23], for tunnel inspections [24], detecting and assessing pipes [25], in the inspection and condition assessment of bridges [26], [27], for forensic applications [28], mineral exploration [29], and airborne GPR [30]. In the case of GPR numerical modeling for landmine detection, generic types of antennas over simple targets buried in both homogenous and inhomogeneous soils have been modeled by [31]- [34].…”

Section: Introductionmentioning

confidence: 99%

A Realistic FDTD Numerical Modeling Framework of Ground Penetrating Radar for Landmine Detection

Giannakis

Giannopoulos

Warren

2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

161

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A three-dimensional (3-D) finite-difference timedomain (FDTD) algorithm is used in order to simulate ground penetrating radar (GPR) for landmine detection. Two bowtie GPR transducers are chosen for the simulations and two widely employed antipersonnel (AP) landmines, namely PMA-1 and PMN are used. The validity of the modeled antennas and landmines is tested through a comparison between numerical and laboratory measurements. The modeled AP landmines are buried in a realistically simulated soil. The geometrical characteristics of soil's inhomogeneity are modeled using fractal correlated noise, which gives rise to Gaussian semivariograms often encountered in the field. Fractals are also employed in order to simulate the roughness of the soil's surface. A frequency-dependent complex electrical permittivity model is used for the dielectric properties of the soil, which relates both the velocity and the attenuation of the electromagnetic waves with the soil's bulk density, sand particles density, clay fraction, sand fraction, and volumetric water fraction. Debye functions are employed to simulate this complex electrical permittivity. Background features like vegetation and water puddles are also included in the models and it is shown that they can affect the performance of GPR at frequencies used for landmine detection (0.5-3 GHz). It is envisaged that this modeling framework would be useful as a testbed for developing novel GPR signal processing and interpretations procedures and some preliminary results from using it in such a way are presented.

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Synthetic GPR modelling studies on shallow geological properties and its comparison with the real data

Cited by 2 publications

References 9 publications

GPR modelling and inversion to quantify the debris content within ice

GPR modelling and inversion to quantify the debris content within ice

A Realistic FDTD Numerical Modeling Framework of Ground Penetrating Radar for Landmine Detection

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