On the use of dispersive APVO GPR curves for thin-bed properties estimation: Theory and application to fracture characterization

Abstract

International audienceThe presence of a thin layer embedded in any formation creates complex reflection patterns caused by interferences within the thin bed. The generated reflectivity amplitude variations with offset have been increasingly used in seismic interpretation and more recently tested on ground-penetrating radar (GPR) data to characterize nonaqueous-phase liquid contaminants. Phase and frequency sensitivities of the reflected signals are generally not used, although they contain useful information. The present study aims to evaluate the potential of these combined properties to characterize a thin bed using GPR data acquired along a common-midpoint (CMP) survey, carried out to assess velocity variations in the ground. It has been restricted to the simple case of a thin bed embedded within a homogeneous formation, a situation often encountered in fractured media. Dispersive properties ofthe dielectric permittivity of investigated materials (homogeneous formation, thin bed) are described using a Jonscher parameterization, which permitted study of the dependency of amplitude and phase variation with offset (APVO) curves on frequency and thin-bed properties (filling nature, aperture). In the second part, we discuss and illustrate the validity of the thin-bed approximation as well as simplify assumptions and make necessary careful corrections to convert raw CMP data into dispersive APVO curves. Two different strategies are discussed to correct the data from propagation effects: a classical normal-moveout approach and an inverse method. Finally, the proposed methodology is applied to a CMP GPR data set acquired along a vertical cliff. It allowed us to extract the characteristics of a subvertical fracture with satisfying resolution and confidence. The study motivates interest to use dispersion dependency of the reflection coefficient variations for thin-bed characterization

Reflection waveform inversion of ground-penetrating radar data for characterizing thin and ultrathin layers of nonaqueous phase liquid contaminants in stratified media

“…Attribute analysis of GPR data may improve detection of thin layers of subsurface NAPL contamination (Baker, 1998;Orlando, 2002;Bradford and Deeds, 2006;Deparis and Garambois, 2009;Bradford et al, 2010). Attributes include instantaneous phase, instantaneous frequency, and reflection strength.…”

“…For example, Deparis and Garambois (2009) invert for the AVO characteristics of reflection GPR data with respect to frequency and phase. They conclude that a global inversion scheme may improve thin-layer characterization.…”

Detection of near-field, low permittivity layers with ground penetrating radar: Analytical estimation of the reflection coefficient

“…Full waveform inversion methods can give good estimation of thin layers parameters [1][2][3], but they also require an antenna calibration, as well as a large computing cost. The faster alternatives to these methods exploit the reflection coefficient of the layer to determine its properties [4][5][6]. The reflection coefficient, R, is the proportion of the incident wave reflected by the layer.…”

Fracture aperture and fill characterization in a limestone quarry using GPR thin‐layer AVA analysis

“…GPR thin bed reflectivity is sensitive to host media properties (dielectric permittivity, electrical conductivity and magnetic permeability), signal characteristics (frequency, polarization and angle of incidence) and also depends on the thin bed properties i.e. aperture and fill (Hollender and Tillard, 1998, Innan and Innan, 2000, Bradford and Deeds, 2006, Deparis and Garambois, 2009). The sensitivity of thin bed reflectivity has been explored e.g.…”

“…thin beds (Bradford and Deeds, 2006). Deparis and Garambois (2009) studied dispersive frequency dependent amplitude and phase variation with offset (APVO) curves for a restricted case of a thin bed embedded within a homogeneous rock and assessed its potential for characterizing the aperture and fill of such layers. Their approach to estimating thin bed aperture and fill is an inversion scheme which compares in the frequency domain, field data with synthetic data generated from analytical solutions to thin bed reflection coefficients.…”