School of Information Science and Technology, Fudan University, Shanghai, China.
School of Information Science and Technology, Fudan University, Shanghai, China; Yiwu Research Institute of Fudan University, Yiwu, China.
Ultrasonics. 2023 Aug;133:107043. doi: 10.1016/j.ultras.2023.107043. Epub 2023 May 14.
Corrosion quantitative detection of plate or plate-like structure materials is crucial in industrial Non-Destructive Testing (NDT) for determining their remaining life. For doing that, a novel ultrasonic guided wave tomography method, incorporating recurrent neural network (RNN) into full waveform inversion (FWI) called as RNN-FWI, is proposed in this paper. When the wave equation of an acoustic model is solved by a forward model with the cyclic calculation units of an RNN, it is shown that the inversion of the forward model can be obtained iteratively by minimizing a waveform misfit function of quadratic Wasserstein distance between the modeled and measured data. It is also demonstrated that the gradient of the objective function can be obtained by automatic differentiation while the parameters of the waveform velocity model are updated by the adaptive momentum estimation algorithm (Adam). The U-Net deep image prior (DIP) is used as the velocity model regularization in each iteration. The final thickness maps of the plate or plate-like structure materials shown can be archived by the dispersion characteristics of guided waves. Both the numerical simulation and experimental results show that the proposed RNN-FWI tomography method performs better than the conventional time-domain FWI in terms of convergence rate, initial model requirement, and robustness.
板状或板状结构材料的腐蚀定量检测在工业无损检测（NDT）中至关重要，可用于确定其剩余寿命。为此，本文提出了一种将循环神经网络（RNN）纳入全波形反演（FWI）的新型超声导波层析成像方法，称为 RNN-FWI。当利用 RNN 的循环计算单元对声模型的波动方程进行正向模型求解时，表明可以通过最小化模型数据和测量数据之间二次 Wasserstein 距离的波形不匹配函数来迭代地获得正向模型的反演。还证明了在通过自适应动量估计算法（Adam）更新波形速度模型的参数时，可以通过自动微分获得目标函数的梯度，并且在每个迭代中使用 U-Net 深度图像先验（DIP）作为速度模型正则化。最终可以通过导波的频散特性来获得板状或板状结构材料的厚度图。数值模拟和实验结果均表明，所提出的 RNN-FWI 层析成像方法在收敛速度、初始模型要求和鲁棒性方面均优于传统的时域 FWI。
