Full Waveform Inversion of common offset GPR data using a fast deep learning based forward solver

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Electromagnetic (EM) forward solvers, such as the finite-difference time-domain (FDTD) method are an essential part for the interpretation of the GPR data. Their drawback is that they are still computationally expensive algorithms and not easily applicable for simulating real scenarios in the absence of high performance computing (HPC). Machine learning (ML) can provide a solution to this problem for specific applications by providing near real time solutions to the forward problem. In this paper, we have developed an ML-based forward solver that is used in full-waveform inversion (FWI) schemes and is applied to concrete slab scenarios. A model of a real GPR transducer was used in the simulations and as a result the algorithm can be used for the inversion of real data. The coupled ML solver/FWI algorithm was tested with both synthetic and real data to assess its performance. Although the algorithm was tuned for a concrete slab case, it can be adjusted and applied to different GPR applications.

Original languageEnglish
Title of host publication2021 11th International Workshop on Advanced Ground Penetrating Radar, IWAGPR 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665422536
DOIs
Publication statusPublished - 2 Aug 2022
Event11th International Workshop on Advanced Ground Penetrating Radar, IWAGPR 2021 - Valletta, Malta
Duration: 1 Dec 20214 Dec 2021

Conference

Conference11th International Workshop on Advanced Ground Penetrating Radar, IWAGPR 2021
Country/TerritoryMalta
CityValletta
Period1/12/214/12/21

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