A Novel Piecewise Linear Recursive Convolution Approach for Dispersive Media Using the Finite-Difference Time-Domain Method

Peer reviewedPublisher PD

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

Destination brand equity research from 2001 to 2012

The present study delves into a review of the destination brand equity literature published since 2001, aiming to offer tourism researchers a reference guide to the general context, corresponding methods,and focus of previous works. A multisource search resulted in the identification of 64 relevant papers. Content analysis using multiple classifier variables provides further insights into specific geographical, conceptual, and methodological aspects. Conclusions pertain to the multidimensional character of the construct, the methodology, and context in which destination brand performance has been developed. Destination brand equity appears as a rapidly conceived concept, borrowed from traditional (corporate/product) branding theory, while discussion on its definition and operationalization is still in progress and has yet to mature in a multidisciplinary context. As the first attempt to review destination brand equity within the top tourism and marketing journals and relevant search engines, the study may contribute to a comprehensive overview of the field. The outcomes offer marketing scholars an in-depth view of the concept, providing an overall insight on the various ways destination brands might be evaluated

Higher-Order Convolution PML (CPML) for FDTD Electromagnetic Modelling

A new simple formulation for incorporating a higher-order perfectly matched layer (PML) stretching function within a convolution PML (CPML) implementation in finite-difference time-domain (FDTD) electromagnetic modelling codes is developed. Obtaining in closed form the corresponding time domain impulse response of the inverse of a number of higher-order PML stretching functions enables the efficient and simple implementation of such higher-order PMLs using recursive convolution, in the same way as it was introduced initially for the complex frequency shifted (CFS) PML. This new higher-order CPML exhibits excellent performance that is comparable to the performance shown by other higher-order PML formu-lations whilst it retains the advantage of a relatively simpler implementation

A Comprehensive Forward Model for Imaging under Irregular Terrain Using RF Tomography

Imaging of tunnel networks under irregular terrain using RF tomography is generalized to include the possibility of magnetic dipoles (i.e., electric loops) either as transmitting or receiving devices. Forward scattering models are presented, and a generalized method for computing numerical dyadic Green’s functions is detailed. Explicit formulas for fast numerical implementation are also presented. The paper is corroborated with numerical simulations aimed at validating formulas

Multipole Perfectly Matched Layer for Finite-Difference Time-Domain electromagnetic modelling

A new multipole perfectly matched layer (PML) formulation is presented. Based on the stretched-coordinate approach the formulation, that utilises a recursive integration concept in its development, introduces a PML stretching function that is created as the sum of any given number of complex- frequency shifted (CFS) constituent poles. Complete formulae for up to a 3-pole formulation, to facilitate its implementation in finite-difference time-domain (FDTD) codes, are developed. The performance of this new multipole formulation compares favourably with existing higher order PMLs that instead utilise stretching functions that are developed as the product of elemen- tary CFS constituent poles. It is argued that the optimisation of the new multipole PML could be more straightforward when compared to that of a higher order PML due to the absence of extra terms generated by the process of multiplication used in the development of the overall PML stretching function in higher order PMLs. The new multipole PML is found to perform very well when compared to standard CFS-PMLs requiring equivalent computational resources

A higher order perfectly matched layer formulation for finite-difference time-domain seismic wave modeling

We have developed a higher order perfectly matched layer (PML) formulation to improve the absorption performance for finite-difference time-domain seismic modeling. First, we outlined a new unsplit “correction” approach, which allowed for traditional, first-order PMLs to be added directly to existing codes in a straightforward manner. Then, using this framework, we constructed a PML formulation that can be used to construct higher order PMLs of arbitrary order. The greater number of degrees of freedom associated with the higher order PML allow for enhanced flexibility of the PML stretching functions, thus potentially facilitating enhanced absorption performance. We found that the new approach can offer increased elastodynamic absorption, particularly for evanescent waves. We also discovered that the extra degrees of freedom associated with the higher order PML required careful optimization if enhanced absorption was to be achieved. Furthermore, these extra degrees of freedom increased the computational requirements in comparison with first-order schemes. We reached our formulations using one compact equation thus increasing the ease of implementation. Additionally, the formulations are based on a recursive integration approach that reduce PML memory requirements, and do not require special consideration for corner regions. We tested the new formulations to determine their ability to absorb body waves and surface waves. We also tested standard staggered grid stencils and rotated staggered grid stencils

Numerical Modelling of Ground Penetrating Radar Antennas

Peer reviewe

An Advanced GPR Modelling Framework: The Next Generation of gprMax

ACKNOWLEDGMENT The authors would like to acknowledge financial support for this work from The Defence Science and Technology Laboratory (Dstl), UK. This work has made use of the resources provided by the Edinburgh Compute and Data Facility (ECDF) (http://www.ecdf.ed.ac.uk/). This work benefited from networking activities carried out within the EU funded COST Action TU1208 “Civil Engineering Applications of Ground Penetrating Radar

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