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

Iraklis Giannakis, Antonios Giannopoulos, Craig Warren

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

The simulation, or forward modeling, of Ground Penetrating Radar (GPR) is becoming a more frequently used approach to facilitate interpretation of complex real GPR data, and as an essential component of full-waveform inversion (FWI). However, general full-wave 3D electromagnetic (EM) solvers, such as 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 which combines a predictive principal component analysis technique, a detailed model of the GPR transducer, and a large dataset 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 datasets. The combination of the near real-time computation, which is orders of magnitude less than what is achievable by traditional full-wave 3D EM solvers, and the accuracy of our ML-based forward model is a significant step towards commercially-viable applications of FWI of GPR.
Original languageEnglish
JournalIEEE Transactions on Geoscience and Remote Sensing
Early online date31 Jan 2019
Publication statusE-pub ahead of print - 31 Jan 2019

Keywords / Materials (for Non-textual outputs)

  • Machine learning
  • ground penetrating radar
  • modelling
  • Computational modeling
  • Training
  • Data models
  • Neural Networks
  • finite difference method
  • time-domain analysis
  • Concrete
  • Deep learning
  • finite-difference time-domain (FDTD)
  • full-waveform inversion
  • Non-destructive testing
  • rebar


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