Joint inversion of full-waveform GPR and ER data enhanced by the envelope transform and cross-gradients

Domenzain, Diego; Bradford, John H.; Mead, Jodi

doi:10.1190/gpr2020-087.1

Cited by 1 publication

(1 citation statement)

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“…Among the ground-penetrating radars with various mechanisms, the two-dimensional ground-penetrating radar can only collect one vertical section data at a time when detecting pavement engineering diseases, which can only make qualitative analysis of diseases and is easy to misjudge and miss diseases. Three-dimensional ground-penetrating radar adopts multi-antenna setting and multi-section data combination judgment, which has the advantages of full coverage, high accuracy of disease judgment, and quantitative analysis of diseases (Allroggen and Tronicke, 2020;Domenzain et al, 2020;Tosti and Ferrante, 2020). In this article, 3D GPR images are used to carry out automatic recognition of void images.…”

Section: Prefacementioning

confidence: 99%

Research on Void Signal Recognition Algorithm of 3D Ground-Penetrating Radar Based on the Digital Image

Huang

Tang³

et al. 2022

Front. Mater.

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The three-dimensional ground-penetrating radar system is an effective method to detect road void disease. Ground penetrating radar image interpretation has the characteristics of multi-solution, long interpretation period, and high professional requirements of processors. In recent years, researchers have put forward solutions for automatic interpretation of ground-penetrating radar images, including automatic detection algorithm for subgrade diseases based on support vector machines, etc., but there are still some shortcomings such as training models with a large amount of data or setting parameters. In this article, a three-dimensional ground-penetrating radar void signal recognition algorithm based on the digital image is proposed, and the algorithm uses digital images to characterize radar signals. With the help of digital image processing methods, the images are processed by binarization, corrosion, expansion, connected area inspection, fine length index inspection, and three-dimensional matching inspection, so as to identify and determine the void signals and extract the void area volume index. The algorithm has been verified by laboratory tests and engineering projects, and the results show that the void identification algorithm can accurately identify the void area position; the error level between the measured values and the measured values of length, width, buried depth, and area is between 2.2 and 17.3%, and the error is generally within the engineering acceptance range. The volume index calculated by the algorithm has a certain engineering application value; compared with the support vector machine, the regressive convolution neural network, and other recognition methods, it has the advantage of not needing a large amount of data to train or modify parameters.

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Section: Prefacementioning

confidence: 99%