Inversion of Ground Penetrating Radar Data Based on Neural Networks

Liu, Tao; Su, Yi; Huang, Chunlin

doi:10.3390/rs10050730

Cited by 27 publications

(22 citation statements)

References 21 publications

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“…There is a trend in using artificial intelligence (AI) in automatic processing and detection instead of the common detailed trace-to-trace processing and subjective data interpretation. During the last decade, artificial neural networks associated with machine learning techniques have been successfully applied to improve the GPR processing [342], namely, the inversion approach to locate subsurface targets [343,344], reconstructing high-quality relative permittivity maps of tunnel lining [345], rebar detection [346], and railway ballast diagnosis through unsupervised processing [347,348].…”

Section: Final Remarks and Future Perspectivesmentioning

confidence: 99%

A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

2021

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The non-destructive testing and diagnosis of transport infrastructures is essential because of the need to protect these facilities for mobility, and for economic and social development. The effective and timely assessment of structural health conditions becomes crucial in order to assure the safety of the transportation system and time saver protocols, as well as to reduce excessive repair and maintenance costs. Ground penetrating radar (GPR) is one of the most recommended non-destructive methods for routine subsurface inspections. This paper focuses on the on-site use of GPR applied to transport infrastructures, namely pavements, railways, retaining walls, bridges and tunnels. The methodologies, advantages and disadvantages, along with up-to-date research results on GPR in infrastructure inspection are presented herein. Hence, through the review of the published literature, the potential of using GPR is demonstrated, while the main limitations of the method are discussed and some practical recommendations are made.

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Section: Final Remarks and Future Perspectivesmentioning

confidence: 99%

A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

2021

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“…An open source software, "gprMax" [23], is a favorable option to generate GPR profiles. It numerically solves Maxwell's equations by the Finite-Difference Time-Domain method [24] and offers advanced subterranean modeling, succeeding in both academic and industrial applications [25][26][27]. In this research, we simulated a stochastic number (range: 0-16) of cylinder pipelines (random diameters ranging from 5 to 40 cm) buried randomly inside a 2 m × 1 m subsurface domain and then produced 40 such GPR profiles (a non-intensive dataset) with downsampled data resolution 400 × 448.…”

Section: Data Descriptionmentioning

confidence: 99%

Wavelet Scattering Network-Based Machine Learning for Ground Penetrating Radar Imaging: Application in Pipeline Identification

Yang

Duan

2020

Remote Sensing

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Automatic and efficient ground penetrating radar (GPR) data analysis remains a bottleneck, especially restricting applications in real-time monitoring systems. Deep learning approaches have good practice in automatic object identification, but their intensive data requirement has reduced their applicability. This paper developed a machine learning framework based on wavelet scattering networks to analyze GPR data for subsurface pipeline identification. Wavelet scattering network is functionally equivalent to convolutional neural networks, and its null-parameter property is intended for non-intensive datasets. A double-channel framework is designed with wavelet scattering networks followed by support vector machines to determine the existence of pipelines on vertical and horizontal traces separately. Classification accuracy rates arrive around 98% and 95% for datasets without and with noises, respectively, as well as 97% for considering surface roughness. Pipeline locations and diameters are convenient to determine from the reconstructed profiles of both simulated and practical GPR signals. However, the results of 5 cm pipelines are sensitive to noises. Nonetheless, the developed machine learning approach presents promising applicability in subsurface pipeline identification.

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“…[31]. It has been applied to target classification for GPR [27]. This method uses a hyper-plane to classify two different samples.…”

Section: Comparison With Classical H-alpha Classification and Supportmentioning

confidence: 99%

Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar

et al. 2019

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The subsurface target classification of ground penetrating radar (GPR) is a popular topic in the field of geophysics. Among the existing classification methods, geometrical features and polarimetric attributes of targets are primarily used. As polarimetric attributes contain more information of targets, polarimetric decomposition methods, such as H-Alpha decomposition, have been developed for target classification of GPR in recent years. However, the classification template used in H-Alpha classification is preset depending on the experience of synthetic aperture radar (SAR); therefore, it may not be suitable for GPR. Moreover, many existing classification methods require excessive human operation, particularly when outliers exist in the sample (the data set containing the features of targets); therefore, they are not efficient or intelligent. We herein propose a new machine learning method based on sample centers, i.e., particle center supported plane (PCSP). The sample center is defined as the point with the smallest sum of distances from all points in the same sample, which is considered as a better representation of the sample without significant effect of the outliers. In this proposed method, particle swarm optimization (PSO) is performed to obtain the sample centers; the new criterion for subsurface target classification is achieved. We applied this algorithm to full polarimetric GPR data measured in the laboratory and outdoors. The results indicate that, comparing with support vector machine (SVM) and classical H-Alpha classification, this new method is more efficient and the accuracy is relatively high.

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Inversion of Ground Penetrating Radar Data Based on Neural Networks

Cited by 27 publications

References 21 publications

A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

Wavelet Scattering Network-Based Machine Learning for Ground Penetrating Radar Imaging: Application in Pipeline Identification

Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar

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