Multilayer ground-penetrating radar guided waves in shallow soil layers for estimating soil water content

Geophysics ◽  
2007 ◽  
Vol 72 (4) ◽  
pp. J17-J29 ◽  
Author(s):  
Claudio Strobbia ◽  
Giorgio Cassiani
Keyword(s):  
Moisture Content ◽  
Ground Penetrating Radar ◽  
Field Data ◽  
Guided Waves ◽  
Single Layer ◽  
Forward Model ◽  
Soil Thickness ◽  
Soil Layers ◽  
Ground Penetrating ◽  
Over Time

The knowledge of moisture-content changes in shallow soil layers has important environmental implications, and ground-penetrating radar (GPR) used in surface-to-surface configuration has been used increasingly to quickly image soil moisture content over large areas. The technique is based on measuring direct GPR wave velocity in the ground. However, in the presence of shallow and thin low-velocity soil layers, dispersive guided GPR waves are generated and the direct ground wave is not identifiable as a simple arrival. Under such conditions, the dispersion relation of guided waves can be estimated from field data and then inverted to obtain the properties of the guiding layers. This approach is applied to a mountain slope with a 1-m soil cover where repeated measurements over time, inverted by conceptualizing the soil as a single guiding layer, lead to estimates of velocity andthickness varying over time. Varying soil thickness clearly is not a plausible physical process. To remove this problem, we develop a multilayer GPR waveguide model. We first assess, using a Monte Carlo sensitivity analysis, the model error arising from using a single-layer forward model to invert data generated by a multilayer waveguide. The single-layer model always underestimates the total soil thickness because the inversion is sensitive mainly to the layer with the lowest velocity (the wettest layer). We then use a multilayer forward model to invert the actual field data. By constraining the total soil thickness, we still manage to invert accurately only for velocity and thickness of the wettest layer, leaving uncertainty about the position of such a layer in the layer sequence. We conclude that these inversion equivalence problems cannot be neglected when guided GPR data are used to estimate time-lapse moisture content in shallow soils.

scholarly journals Identifying Spatial and Temporal Variations in Concrete Bridges with Ground Penetrating Radar Attributes

2021 ◽  
Vol 13 (9) ◽  
pp. 1846
Author(s):  
Vivek Kumar ◽  
Isabel M. Morris ◽  
Santiago A. Lopez ◽  
Branko Glisic
Keyword(s):  
Compressive Strength ◽  
Ground Penetrating Radar ◽  
Material Properties ◽  
Temporal Variations ◽  
Concrete Bridges ◽  
Spatial And Temporal Variation ◽  
Reflected Waves ◽  
Ground Penetrating ◽  
Over Time

Estimating variations in material properties over space and time is essential for the purposes of structural health monitoring (SHM), mandated inspection, and insurance of civil infrastructure. Properties such as compressive strength evolve over time and are reflective of the overall condition of the aging infrastructure. Concrete structures pose an additional challenge due to the inherent spatial variability of material properties over large length scales. In recent years, nondestructive approaches such as rebound hammer and ultrasonic velocity have been used to determine the in situ material properties of concrete with a focus on the compressive strength. However, these methods require personnel expertise, careful data collection, and high investment. This paper presents a novel approach using ground penetrating radar (GPR) to estimate the variability of in situ material properties over time and space for assessment of concrete bridges. The results show that attributes (or features) of the GPR data such as raw average amplitudes can be used to identify differences in compressive strength across the deck of a concrete bridge. Attributes such as instantaneous amplitudes and intensity of reflected waves are useful in predicting the material properties such as compressive strength, porosity, and density. For compressive strength, one alternative approach of the Maturity Index (MI) was used to estimate the present values and compare with GPR estimated values. The results show that GPR attributes could be successfully used for identifying spatial and temporal variation of concrete properties. Finally, discussions are presented regarding their suitability and limitations for field applications.

Measurement of Bulk Electrical Properties Using GPR with a Variable Reflector

2018 ◽  
Vol 23 (4) ◽  
pp. 489-496
Author(s):  
J. David Redman ◽  
A. Peter Annan ◽  
Nectaria Diamanti
Keyword(s):  
Electrical Properties ◽  
Moisture Content ◽  
Ground Penetrating Radar ◽  
Arrival Time ◽  
Wood Chip ◽  
Direct Wave ◽  
Trace Data ◽  
Ground Penetrating ◽  
Prototype Instrument

Bulk electrical properties of media are important inherently for ground penetrating radar (GPR) applications and for providing a means to determine indirectly other physical properties such as moisture content. We have developed a reflector whose reflectivity can be controlled electronically. This variable reflector controlled by a GPR provides an effective method to measure bulk electrical properties of media. For sample measurements, the GPR is placed on one side of a sample and the variable reflector on the opposite side. GPR trace data are then acquired with the reflector in an on-state and in the off-state. By differencing these measurements, we improve the ability to detect the specific reflection event from the variable reflector. This process removes both the direct wave and clutter from the trace data, improving the quality of the refection event and our ability to accurately pick its arrival time and amplitude. We describe the variable reflector, a prototype instrument based on the reflector and numerical modeling performed to understand its response. We also show the results of testing applications to the measurement of wood chip moisture content and monitoring of the electrical properties of concrete during the curing process.

scholarly journals Non-destructive evaluation of moisture content in wood by using Ground Penetrating Radar

2016 ◽  
Author(s):  
Hamza Reci ◽  
Tien Chinh Maï ◽  
Zoubir Mehdi Sbartaï ◽  
Lara Pajewski ◽  
Emanuela Kiri
Keyword(s):  
Moisture Content ◽  
Ground Penetrating Radar ◽  
Wood Fiber ◽  
Fiber Direction ◽  
Direct Wave ◽  
Reflected Waves ◽  
Moisture Variation ◽  
Reflection Configuration ◽  
Ground Penetrating ◽  
Non Destructive

Abstract. This paper presents the results of a series of laboratory measurements carried out to study how the Ground Penetrating Radar (GPR) signal is affected by moisture variation in wood material. The effects of the wood fiber direction, with respect to the polarisation of the electromagnetic field, are investigated. The relative permittivity of wood and the amplitude of the electric field received by the radar are measured for different humidity levels, by using the direct-wave method in Wide Angle Radar Reflection configuration, where one GPR antenna is moved while the other is kept in a fixed position. The received signal is recorded for different separations between transmitting and receiving antennas. Direct waves are compared to reflected waves: it is observed that they show a different behaviour when the moisture content varies, due to their different propagation paths.

Exploring the Japan Cave in Taman Hutan Raya Djuanda, Bandung Using GPR

2018 ◽  
Vol 23 (3) ◽  
pp. 377-381
Author(s):  
Widodo Widodo ◽  
Azizatun Azimmah ◽  
Djoko Santoso
Keyword(s):  
Dielectric Permittivity ◽  
Ground Penetrating Radar ◽  
Field Data ◽  
Geophysical Methods ◽  
Forward Modeling ◽  
Additional Line ◽  
Reflection Amplitude ◽  
Underground Cavities ◽  
Ground Penetrating ◽  
Synthetic Models

Investigating underground cavities is vital due to their potential for subsidence and total collapse. One of the proven geophysical methods for locating underground cavities at a shallow depth is ground penetrating radar (GPR). GPR uses contrasting dielectric permittivity, resistivity, and magnetic permeability to map the subsurface. The aim of this research is to prove that GPR can be applied to detect underground cavities in the Japan Cave of Taman Hutan Raya Djuanda, in Bandung, Indonesia. Forward modeling was performed first using three representative synthetic models before field data were acquired. The data acquisition was then conducted using a 100 MHz GPR shielded antenna with three lines of 80 m and one additional line 10 m long. The result showed a region of different reflection amplitude, which was proven to be the air-filled cavities.

Diffraction imaging of ground-penetrating radar data

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. H1-H12 ◽  
Author(s):  
Hemin Yuan ◽  
Mahboubeh Montazeri ◽  
Majken C. Looms ◽  
Lars Nielsen
Keyword(s):  
Ground Penetrating Radar ◽  
Field Data ◽  
Velocity Structure ◽  
Velocity Estimation ◽  
Small Scale ◽  
Imaging Method ◽  
Surface Reflection ◽  
Diffraction Imaging ◽  
Saturated Media ◽  
Ground Penetrating

Diffractions caused by, e.g., faults, fractures, and small-scale heterogeneity localized near the surface are often used in ground-penetrating radar (GPR) reflection studies to constrain the subsurface velocity distribution using simple hyperbola fitting. Interference with reflected energy makes the identification of diffractions difficult. We have tailored and applied a diffraction imaging method to improve imaging for surface reflection GPR data. Based on a plane-wave destruction algorithm, the method can separate reflections from diffractions. Thereby, a better identification of diffractions facilitates an improved determination of GPR wave velocities and an optimized migration result. We determined the potential of this approach using synthetic and field data, and, for the field study, we also compare the estimated velocity structure with crosshole GPR results. For the field data example, we find that the velocity structure estimated using the diffraction-based process correlates well with results from crosshole GPR velocity estimation. Such improved velocity estimation may have important implications for using surface reflection GPR to map, e.g., porosity for fully saturated media or soil moisture changes in partially saturated media because these physical properties depend on the dielectric permittivity and thereby also the GPR wave velocity.

scholarly journals Evaluation of a Straight-Ray Forward Model for Bayesian Inversion of Crosshole Ground Penetrating Radar Data

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 630 ◽  
Author(s):  
Hui Qin ◽  
Xiongyao Xie ◽  
Yu Tang
Keyword(s):  
Ground Penetrating Radar ◽  
Computational Cost ◽  
Measurement Data ◽  
Forward Model ◽  
Bayesian Inversion ◽  
Inversion Method ◽  
Model Parameters ◽  
Model Structure ◽  
Modeling Error ◽  
Ground Penetrating

Bayesian inversion of crosshole ground penetrating radar (GPR) data is capable of characterizing the subsurface dielectric properties and qualifying the associated uncertainties. Markov chain Monte Carlo (MCMC) simulations within the Bayesian inversion usually require thousands to millions of forward model evaluations for the parameters to hit their posterior distributions. Therefore, the CPU cost of the forward model is a key issue that influences the efficiency of the Bayesian inversion method. In this paper we implement a widely used straight-ray forward model within our Bayesian inversion framework. Based on a synthetic unit square relative permittivity model, we simulate the crosshole GPR first-arrival traveltime data using the finite-difference time-domain (FDTD) and straight-ray solver, respectively, and find that the straight-ray simulator runs 450 times faster than its FDTD counterpart, yet suffers from a modeling error that is more than 7 times larger. We also perform a series of numerical experiments to evaluate the performance of the straight-ray model within the Bayesian inversion framework. With modeling error disregarded, the inverted posterior models fit the measurement data nicely, yet converge to the wrong set of parameters at the expense of unreasonably large number of iterations. When the modeling error is accounted for, with a quarter of the computational burden, the main features of the true model can be identified from the posterior realizations although there still exist some unwanted artifacts. Finally, a smooth constraint on the model structure improves the inversion results considerably, to the extent that it enhances the inversion accuracy approximating to those of the FDTD model, and further reduces the CPU demand. Our results demonstrate that the use of the straight-ray forward model in the Bayesian inversion saves computational cost tremendously, and the modeling error correction together with the model structure constraint are the necessary amendments that ensure that the model parameters converge correctly.

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