In this section
@article{Zuo:2024:10.1098/rspa.2024.0132,
author = {Zuo, P and Huthwaite, P},
doi = {10.1098/rspa.2024.0132},
journal = {Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences},
title = {Guided wave tomography for quantitative thickness mapping using non-dispersive SH0 mode through geometrical full waveform inversion (GFWI)},
url = {http://dx.doi.org/10.1098/rspa.2024.0132},
volume = {480},
year = {2024}
}
TY - JOUR
AB - Guided wave tomography plays a significantly important role in quantitatively mapping thickness variations in plate-like structures and pipelines in the petrochemical industry for accurate measurement of corrosion, as guided waves enable rapid screening of large areas without needing direct access. Several inverse algorithms have been implemented in guided wave tomography; however, almost all of them use a primary assumption: the three-dimensional (3D) guided wave thickness reconstruction is simplified as a two-dimensional (2D) acoustic wave velocity inversion, which is then mapped to thickness variation using the dispersive nature of guided waves. Although this assumption simplifies the inverse procedure, it makes it impossible to use non-dispersive modes in reconstructions. Geometrical full waveform inversion (GFWI) is promising to overcome this limitation since it can reconstruct corrosion profiles through geometry optimization using a data-fitting procedure, where velocity-to-thickness mapping is not needed. In this work, GFWI-based guided wave tomography is developed in plate-like structures, and is applied to reconstruct thickness maps in a series of corrosion defects using the non-dispersive SH0 mode, demonstrating high performance and achieving an improved reconstruction resolution.
AU - Zuo,P
AU - Huthwaite,P
DO - 10.1098/rspa.2024.0132
PY - 2024///
SN - 1364-5021
TI - Guided wave tomography for quantitative thickness mapping using non-dispersive SH0 mode through geometrical full waveform inversion (GFWI)
T2 - Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
UR - http://dx.doi.org/10.1098/rspa.2024.0132
VL - 480
ER -