GPR data processing for fractures and flakes detection in sandstone

Denis, A.; Huneau, Frédéric; Hœrlé, Stéphane; Salomon, Andrew

doi:10.1016/j.jappgeo.2009.01.003

Cited by 20 publications

(8 citation statements)

References 14 publications

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“…The characteristics of underground objects are identified through pseudo-imaging and signal processing. GPR has become a valuable tool in several applications, such as archaeological explorations [1], glacier and ice sheet investigation [2,3], detection and monitoring of below-ground biological structures [3], mineral exploration and resource evaluation [4], building condition assessment [2], road pavement analysis [3,5], and landmine detection [6]. This paper addresses the problem of sparse representation (SR) of GPR signals.…”

Section: Introductionmentioning

confidence: 99%

Sparse Representation of GPR Traces With Application to Signal Classification

Shao

Bouzerdoum

Phung

2013

IEEE Trans. Geosci. Remote Sensing

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Sparse representation (SR) models a signal with a small number of elementary waves using an overcomplete dictionary. It has been employed for a wide range of signal and image processing applications, including denoising, deblurring, and compression. In this paper, we present an adaptive SR method for modeling and classifying ground penetrating radar (GPR) signals. The proposed method decomposes each GPR trace into elementary waves using an adaptive Gabor dictionary. The sparse decomposition is used to extract salient features for SR and classification of GPR signals. Experimental results on real-world data show that the proposed sparse decomposition achieves efficient signal representation and yields discriminative features for pattern classification. © 1980-2012 IEEE.

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Section: Introductionmentioning

confidence: 99%

Sparse Representation of GPR Traces With Application to Signal Classification

Shao

Bouzerdoum

Phung

2013

IEEE Trans. Geosci. Remote Sensing

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“…It can non-destructively detect buried objects beneath the shallow earth surface (less than 50 m) or in a visually impenetrable structure, such as walls and concrete floors. GPR has attracted considerable interest in many areas, such as archaeology [5], road construction [6], glacier and ice sheet investigation [7], and mineral exploration and resource evaluation [8].…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Ground-Penetrating-Radar Signals for Railway-Ballast Assessment

Shao

Bouzerdoum

Phung

et al. 2011

IEEE Trans. Geosci. Remote Sensing

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The ground-penetrating radar (GPR) has been widely used in many applications. However, the processing and interpretation of the acquired signals remain challenging tasks since an experienced user is required to manage the entire operation. In this paper, we present an automatic classification system to assess railway-ballast conditions. It is based on the extraction of magnitude spectra at salient frequencies and their classification using support vector machines. The system is evaluated on real-world railway GPR data. The experimental results show that the proposed method efficiently represents the GPR signal using a small number of coefficients and achieves a high classification rate when distinguishing GPR signals reflected by ballasts of different conditions. KeywordsGround-penetrating radar (GPR) processing, railway-ballast assessment, support vector machine (SVM) Abstract-Ground penetrating radar has been widely used in many applications. However, the processing and interpretation of the acquired signals remain challenging tasks since an experienced user is required to manage the entire operation. In this paper, we present an automatic classification system to assess railway ballast conditions. It is based on the extraction of magnitude spectra at salient frequencies and their classification using support vector machines. The system is evaluated on real-world railway GPR data. The experimental results show that the proposed method efficiently represents the GPR signal using a small number of coefficients, and achieves a high classification rate when distinguishing ground penetrating radar signals reflected by ballast of different conditions. Index Terms-Railway ballast assessment, ground penetrating radar processing, support vector machine. Disciplines Physical Sciences and Mathematics

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“…In contrast, geomorphologists have placed emphasis on understanding contemporary micro-spatio-temporal scales of sandstone weathering in southern Africa, with particular application to understanding the controls on San rock art deterioration (e.g. Hoerlé, 2006;Hall et al, 2007;Denis et al, 2009;Meiklejohn et al, 2009). However, relatively few studies on the sub-continent have examined specific sandstone landforms and their associated process origins, and even less so, made linkages between ancient deformation processes as a control on modern landscape development.…”

Section: Introductionmentioning

confidence: 99%