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

A three-dimensional (3-D) finite-difference time-domain (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

Optimising the complex refractive index model for estimating the permittivity of heterogeneous concrete models

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Realistic FDTD GPR antenna models optimized using a novel linear/nonlinear Full-Waveform Inversion

Finite-Difference Time-Domain (FDTD) modelling of Ground Penetrating Radar (GPR) is becoming regularly used in model-based interpretation methods like full waveform inversion (FWI), and machine learning schemes using synthetic training data. Oversimplifications in such forward models can compromise the accuracy and realism with which real GPR responses can be simulated, and this degrades the overall performance of the aforementioned interpretation techniques. Therefore, a forward model must be able to accurately simulate every part of the GPR problem that can affect the resulting scattered field. A key element is the antenna system and excitation waveform, so the model must contain a complete description of the antenna including the excitation source and waveform, the geometry, and the dielectric properties of materials in the antenna. The challenge is that some of these parameters are not known or easily measured, especially for commercial GPR antennas that are used in practice. We present a novel hybrid linear/non-linear FWI approach which can be used, with only knowledge of the basic antenna geometry, to simultaneously optimise the dielectric properties and excitation waveform of the antenna, and minimise the error between real and synthetic data. The accuracy and stability of our proposed methodology is demonstrated by successfully modelling a Geophysical Survey Systems (GSSI) Inc. 1.5~GHz commercial antenna. Our framework allows accurate models of GPR antennas to be developed without requiring detailed knowledge of every component in the antenna. This is significant because it allows commercial GPR antennas, regularly used in GPR surveys, to be more readily simulated

Electromagnetic modelling and simulation of a high-frequency ground penetrating radar antenna over a concrete cell with steel rods

This work focuses on the electromagnetic modelling and simulation of a highfrequency Ground-Penetrating Radar (GPR) antenna over a concrete cell with reinforcing elements. The development of realistic electromagnetic models of GPR antennas is crucial for accurately predicting GPR responses and for designing new antennas. We used commercial software implementing the Finite-Integration technique (CST Microwave Studio) to create a model that is representative of a 1.5 GHz Geophysical Survey Systems, Inc. antenna, by exploiting information published in the literature (namely, in the PhD Thesis of Dr Craig Warren); our CST model was validated, in a previous work, by comparisons with FiniteDifference Time-Domain results and with experimental data, with very good agreement, showing that the software we used is suitable for the simulation of antennas in the presence of targets in the near field. In the current paper, we firstly describe in detail how the CST model of the antenna was implemented; subsequently, we present new results calculated with the antenna over a reinforced-concrete cell. Such cell is one of the reference scenarios included in the Open Database of Radargrams of COST Action TU1208 “Civil engineering applications of Ground Penetrating Radar” and hosts five circular-section steel rods, having different diameters, embedded at different depths into the concrete. Comparisons with a simpler model, where the physical structure of the antenna is not taken into account, are carried out; the significant differences between the results of the realistic model and the results of the simplified model confirm the importance of including accurate models of the actual antennas in GPR simulations; they also emphasize how salient it is to remove antenna effects as a pre-processing step of experimental GPR data. The simulation results of the antenna over the concrete cell presented in this paper are attached to the paper as ‘Supplementary materials.

A Machine Learning Approach For Simulating Ground Penetrating Radar

The ability to produce, store and analyse large amounts of well-labeled data as well as recent advancements on supervised training, led machine learning to gain a renewed popularity. In the present paper, the applicability of machine learning to simulate ground penetrating radar (GPR) for high frequency applications is examined. A well-labelled and equally distributed training set is generated synthetically using the finite difference time-domain (FDTD) method. Special care was taken in order to model the antennas and the soils with sufficient accuracy. Through a stochastic parameterisation, each model is expressed using only seven parameters (i.e. the fractal dimension of water fraction, the heigh of the antenna and so on). Based on these parameters and the synthetically generated training set, a machine learning framework is trained to predict the resulting A-Scan in real-time. Thus, overcoming the time-consuming calculations required for an equivalent FDTD simulation

A Machine Learning Based Fast Forward Solver for Ground Penetrating Radar with Application to Full Waveform Inversion

The simulation, or forward modeling, of ground penetrating radar (GPR) is becoming a more frequently used approach to facilitate the interpretation of complex real GPR data, and as an essential component of full-waveform inversion (FWI). However, general full-wave 3-D electromagnetic (EM) solvers, such as the ones based on the finite-difference time-domain (FDTD) method, are still computationally demanding for simulating realistic GPR problems. We have developed a novel near-real-time, forward modeling approach for GPR that is based on a machine learning (ML) architecture. The ML framework uses an innovative training method that combines a predictive principal component analysis technique, a detailed model of the GPR transducer, and a large data set of modeled GPR responses from our FDTD simulation software. The ML-based forward solver is parameterized for a specific GPR application, but the framework can be applied to many different classes of GPR problems. To demonstrate the novelty and computational efficiency of our ML-based GPR forward solver, we used it to carry out FWI for a common infrastructure assessment application--determining the location and diameter of reinforcement bars in concrete. We tested our FWI with synthetic and real data and found a good level of accuracy in determining the rebar location, size, and surrounding material properties from both data sets. The combination of the near-real-time computation, which is orders of magnitude less than what is achievable by traditional full-wave 3-D EM solvers, and the accuracy of our ML-based forward model is a significant step toward commercially viable applications of FWI of GPR

Model-Based Evaluation of Signal-to-Clutter Ratio for Landmine Detection Using Ground-Penetrating Radar

A regression model is developed in order to estimate in real time the signal-to-clutter ratio (SCR) for landmine detection using ground-penetrating radar. Artificial neural networks are employed in order to express SCR with respect to the soil's properties, the depth of the target, and the central frequency of the pulse. The SCR is synthetically evaluated for a wide range of diverse and controlled scenarios using the finite-difference time-domain method. Fractals are used to describe the geometry of the soil's heterogeneities as well as the roughness of the surface. The dispersive dielectric properties of the soil are expressed with respect to traditionally used soil parameters, namely, sand fraction, clay fraction, water fraction, bulk density, and particle density. Through this approach, a coherent and uniformly distributed training set is created. The overall performance of the resulting nonlinear function is evaluated using scenarios which are not included in the training process. The calculated and the predicted SCR are in good agreement, indicating the validity and the generalization capabilities of the suggested framework

Fractal-Constrained Crosshole/Borehole-to-Surface Full Waveform Inversion for Hydrogeological Applications Using Ground-Penetrating Radar

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