State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, 92 Weijin Road, Tianjin 300072, China.
State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, 92 Weijin Road, Tianjin 300072, China; Department of Mechanical Engineering, University of Wyoming, Laramie, WY 82071, United States of America.
Ultrasonics. 2022 May;122:106686. doi: 10.1016/j.ultras.2022.106686. Epub 2022 Feb 7.
Machine learning has been demonstrated to be extremely promising in solving inverse problems, but deep learning algorithms require enormous training samples to obtain reliable results. In this article, we propose a new solution, the deep learning inversion with supervision (DLIS) and applied it for corrosion mapping in guided wave tomography. The inversion results show that when dealing with multiple defects of complex shape on a plate-like structure, DLIS methods can reduce the scale of training set effectively compared with other deep learning algorithms in experiment because a good starting model is provided and the nonlinearity between the global minimum and observed wave field is greatly reduced. In terms of reconstruction accuracy using experimental data, the thickness maps produced by DLIS are reliable with high accuracy. With few modifications, this method can be conveniently extended to 3D cases. These results imply that DLIS is one of the promising methods to be applied in fields with similar physics like non-destructive evaluation (NDE), biomedical imaging and geophysical prospecting.
机器学习在解决反问题方面表现出了极大的潜力，但深度学习算法需要大量的训练样本才能得到可靠的结果。在本文中，我们提出了一种新的解决方案，即带监督的深度学习反演（DLIS），并将其应用于导波层析成像中的腐蚀测绘。反演结果表明，在处理板状结构上多个复杂形状的缺陷时，与其他深度学习算法相比，DLIS 方法可以在实验中有效地减小训练集的规模，因为它提供了一个良好的初始模型，并且大大降低了全局最小值与观测波场之间的非线性关系。在使用实验数据进行重建准确性方面，DLIS 产生的厚度图具有高精度的可靠性。经过少量修改，该方法可以方便地扩展到 3D 情况。这些结果表明，DLIS 是一种很有前途的方法，可以应用于类似物理领域，如无损评估（NDE）、生物医学成像和地球物理勘探。
