A machine learning based fast forward solver for ground penetrating radar with application to full waveform inversion

Giannakis, Iraklis, Giannopoulos, Antonios and Warren, Craig (2019) A machine learning based fast forward solver for ground penetrating radar with application to full waveform inversion. IEEE Transactions on Geoscience and Remote Sensing, 57 (7). pp. 4417-4426. ISSN 0196-2892

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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 fora common infrastructure assessment application – determining the location and diameter of reinforcement bars in concrete. We tested our FWI with synthetic and real data, and founda 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-wave3D EM solvers, and the accuracy of our ML-based forward modelis a significant step towards commercially-viable applications of FWI of GPR.

Item Type: Article
Identifier: 10.1109/tgrs.2019.2891206
Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Concrete, Deep learning, FDTD, FWI, GPR, Machine Learning, NDT, Neural Networks, Rebar
Subjects: Construction and engineering > Electrical and electronic engineering
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Depositing User: Iraklis Giannakis
Date Deposited: 19 Jan 2019 16:55
Last Modified: 06 Feb 2024 15:59
URI: https://repository.uwl.ac.uk/id/eprint/5754


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