TY - JOUR
T1 - Realistic FDTD GPR antenna models optimized using a novel linear/nonlinear Full-Waveform Inversion
AU - Giannakis, Iraklis
AU - Giannopoulos, Antonios
AU - Warren, Craig
PY - 2019/3
Y1 - 2019/3
N2 - 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.
AB - 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.
KW - Antennas
KW - commercial
KW - Dielectrics
KW - Finite difference methods
KW - finite-difference time-domain (FDTD)
KW - full-waveform inversion (FWI)
KW - Ground penetrating radar
KW - ground-penetrating radar (GPR)
KW - hybrid optimization
KW - linear inversion
KW - nonlinear inversion
KW - Numerical models
KW - Optimization
KW - Time-domain analysis
U2 - 10.1109/TGRS.2018.2869027
DO - 10.1109/TGRS.2018.2869027
M3 - Article
VL - 57
SP - 1768
EP - 1778
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
SN - 0196-2892
IS - 3
ER -