On ultrasonic guided wave tomography using a decoder architecture for sparse sensor array data

Tools

Matthaiou, Ioannis and Tant, Katy and Dobie, Gordon and McInnes, Matthew and Dick, Cameron and Ashok, Praveen and Hughes, Dave Allan; (2025) On ultrasonic guided wave tomography using a decoder architecture for sparse sensor array data. In: 2025 IEEE International Ultrasonics Symposium (IUS). 2025 IEEE International Ultrasonics Symposium (IUS) . IEEE, NLD, pp. 1-4. ISBN 979-8-3315-2332-9 (https://doi.org/10.1109/ius62464.2025.11201281)

[thumbnail of Matthaiou-etal-2025-On-ultrasonic-guided-wave-tomography]
Preview
 Text. Filename: Matthaiou-etal-2025-On-ultrasonic-guided-wave-tomography.pdf
Accepted Author Manuscript
License: Creative Commons Attribution 4.0 logo
Download (3MB)| Preview

Abstract

We address defect mapping from sparse guided-wave measurements by learning to reconstruct 2-D velocity fields from eight travel-time paths. Our geometry-conditioned model is trained on a large synthetic dataset matched to a specific experimental setup. In simulation, reconstruction quality is highest when thinning overlaps the transmitter. On the experiment, the model recovers two corrosion regions; depth agrees (< 1mm), while x-y localisation is centre-biased. A fundamental limitation is the sim-to-real gap. Overall, eight paths suffice for coarse, depth-consistent maps with approximate estimation of x-y location.

ORCID iDs

Matthaiou, Ioannis ORCID logoORCID: https://orcid.org/0009-0009-3603-2999, Tant, Katy, Dobie, Gordon ORCID logoORCID: https://orcid.org/0000-0003-3972-5917, McInnes, Matthew, Dick, Cameron, Ashok, Praveen and Hughes, Dave Allan;
CORE (COnnecting REpositories)