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Comparative Study of Deconvolution Algorithms for GPR Bridge Deck Imaging

机译：GPR桥面成像反卷积算法的比较研究

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摘要

Bridge decks deteriorate over time as a result of freezing-and-thawing, heavy use, and water penetration resulting in internal defects. Ground penetrating radar (GPR) can be used as a non-destructive method for detecting such defects. Unfortunately, reflections from closely spaced objects overlap which prevents the accurate estimation of the round-trip travel time of GPR waves to the closely spaced objects. In this paper, singular value decomposition (SVD), subset selection (SSDA), and independent component analysis (ICA) deconvolution algorithms are used to solve this problem using GPR scans of simulated concrete bridge decks. Then, velocity analysis method is used to estimate depth of defects as an evaluation criterion. Results show that ICA has better performance than SVD and SSDA at a cost of slower execution time.
机译：由于冻融，大量使用和渗水导致内部缺陷，桥面板会随着时间的流逝而变质。探地雷达（GPR）可以用作检测此类缺陷的非破坏性方法。不幸的是，来自紧密间隔的物体的反射重叠，这妨碍了精确估计GPR波到紧密间隔的物体的往返传播时间。在本文中，奇异值分解（SVD），子集选择（SSDA）和独立分量分析（ICA）反卷积算法用于通过模拟混凝土桥面板的GPR扫描解决此问题。然后，使用速度分析方法估计缺陷的深度作为评估标准。结果表明，ICA具有比SVD和SSDA更好的性能，但执行时间较慢。

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