Finite-difference time-domain forward modeling of ground-penetrating radar (GPR) is becoming regularly used in model-based interpretation methods, such as full-waveform inversion (FWI) and machine learning schemes. Oversimplifications in such forward models can compromise the accuracy and realism with which real GPR responses can be simulated, which degrades the overall performance of interpretation techniques. A forward model must be able to accurately simulate every part of the GPR problem that affects the resulting scattered field. A key element, especially for near-field applications, is the antenna system. Therefore, the model must contain a complete description of the antenna, including the excitation source and waveform, the geometry, and the dielectric properties of any materials in the antenna. The challenge is that some of these parameters are not known or cannot be easily measured, especially for commercial GPR antennas that are used in practice. We present a novel hybrid linear/nonlinear FWI approach that can be used, with only knowledge of the basic antenna geometry, to simultaneously optimize the dielectric properties and excitation waveform of the antenna and minimize the error between real and synthetic data. The accuracy and stability of our proposed methodology are demonstrated by successfully modeling a 1.5-GHz commercial antenna from Geophysical Survey Systems, Inc. Our framework allows accurate models of GPR antennas to be developed without requiring detailed knowledge of every component of the antenna. This is significant because it allows commercial GPR antennas, regularly used in GPR surveys, to be more readily simulated.
|Number of pages||21|
|Journal||IEEE Transactions on Geoscience and Remote Sensing|
|Early online date||27 Sep 2018|
|Publication status||Published - Mar 2019|
Giannakis, I., Giannopoulos, A., & Warren, C. (2019). Realistic FDTD GPR Antenna Models Optimized Using a Novel Linear/Nonlinear Full-Waveform Inversion. IEEE Transactions on Geoscience and Remote Sensing, 57(3), 1768-1778. https://doi.org/10.1109/TGRS.2018.2869027