Investigation of the reconstruction accuracy of guided wave tomography using full waveform inversion

Quantitative guided wave thickness mapping in plate-like structures and pipelines is of significant importance for the petrochemical industry to accurately estimate the minimum remaining wall thickness in the presence of corrosion, as guided waves can inspect a large area without needing direct access. Although a number of inverse algorithms have been studied and implemented in guided wave reconstruction, a primary assumption is widely used: the three-dimensional guided wave inversion of thickness is simplified as a two-dimensional acoustic wave inversion of velocity, with the dispersive nature of the waves linking thickness to velocity. This assumption considerably simplifies the inversion procedure; however, it makes it impossible to account for mode conversion. In reality, mode conversion is quite common in guided wave scattering with asymmetric wall loss, and compared with non-converted guided wave modes, converted modes may provide greater access to valuable information about the thickness variation, which, if exploited, could lead to improved performance. Geometrical full waveform inversion (GFWI) is an ideal tool for this, since it can account for mode conversion. In this paper, quantitative thickness reconstruction based on GFWI is developed in a plate cross-section and applied to study the performance of thickness reconstruction using mode conversion.

Download Full-text

Robust Guided Wave Tomography Method for Large and Irregular Defects

10.1115/qnde2021-75023 ◽

2021 ◽

Author(s):

Junkai Tong ◽

Min Lin ◽

Xiaocen Wang ◽

Jiahao Ren ◽

Jian Li ◽

...

Keyword(s):

Complex Shape ◽

Waveform Inversion ◽

Guided Wave ◽

Diffraction Tomography ◽

Multiple Defects ◽

Full Waveform ◽

Speed Advantage ◽

Wave Tomography ◽

Tomography Method ◽

Defect Imaging

Abstract Finding a fast, robust way to quantitatively measuring the remaining wall thickness of complex structures when multiple defects exist is one of the leading challenges in Nondestructive Testing (NDT). Traditional inversion algorithms like ray tomography and full waveform inversion (FWI) suffered from problems like convergence, limited resolution and slow speed. Diffraction tomography (DT) has speed advantage over the preceding methods and its resolution can be further amplified by integrating with other methods like bent-ray tomography and iteration. However, DT can only detect shallow and small defects. Compared with those methods, convolutional neural network (CNN) opens a new way for quantitative defect imaging, as with pre-trained data it can achieve significant speed and resolution than the traditional methods. In this paper, we investigated the performance of CNN in imaging multiple defects and the inversion results show that when dealing with multiple defects with complex shape on a plate-like structure, CNN can achieve better resolution than other methods with maximum errors below 0.54mm in most regions. This research provides the experimental guidance for future study in finding the possible ways to improve the resolution of the algorithms.

Download Full-text