Ground Penetrating Radar Data Processing, Modelling and Analysis

Cassidy, Nigel J.

doi:10.1016/b978-0-444-53348-7.00005-3

Cited by 143 publications

(138 citation statements)

References 50 publications

Supporting

Mentioning

124

Contrasting

Unclassified

Order By: Relevance

“…Na Figura 5, são apresentados exemplos de ruídos que podem ser removidos com a aplicação desse filtro. As frequências de entrada exigidas pelo processamento foram escolhidas a partir de uma banda de sinal que vai de 1/2 a 3/2 da frequência central da antena (Cassidy, 2009 …”

Section: Band Pass Frequencyunclassified

Imageamento digital e modelagem virtual 3D de um bloco de rocha travertina

Silva

Castro

Souza

et al. 2014

Geol. USP, Sér. cient.

View full text Add to dashboard Cite

Disponível on-line no endereço www.igc.usp.br/geologiausp -75 -Resumo Neste trabalho são apresentados os procedimentos metodológicos envolvidos no imageamento digital de um bloco de rocha travertina quaternária em uma escala não convencional, pesando 21,2 ton e medindo 1,60 x 1,60 x 2,70 m, que se encontra no Laboratório Experimental de Petróleo "Kelsen Valente" (LabPetro), da Universidade Estadual de Campinas (UNICAMP). O bloco, denominado de T-Block, foi disponibilizado pela Petrobras com o objetivo de permitir o seu imageamento digital e a realização de ensaios petrofísicos por pesquisadores de universidades e centros de pesquisa brasileiros que atuem nas áreas de caracterização de reservatório. A proposta deste trabalho consiste na elaboração de um Modelo Sólido Digital, envolvendo técnicas de imageamento digital 3D das superfícies interna e externa do T-Block. Para o imageamento das superfícies externas, foi utilizada a tecnologia Light Detection and Range (LIDAR), enquanto que para o imageamento das superfícies internas foi utilizado o Ground Penetrating Radar (GPR). Além disso, foram adquiridos perfis de raios gama com um gamaespectrômetro portátil. O uso da tecnologia Light Detection and Range associada ao Ground Penetrating Radar possibilitou a identificação e mapeamento 3D de três radarfácies distintas, as quais foram correlacionadas a três fácies sedimentares identificadas: "Radarfácies Travertino em Domos", "Radarfácies Travertino Transicional" e "Radarfácies Travertino Laminado". Os perfis de raios gama mostraram uma baixa variação na amplitude dos valores de radioatividade, provavelmente, em razão de as camadas sedimentares perfiladas apresentarem a mesma composição mineralógica (carbonática), sem argila siliciclástica e/ou minerais radioativos, nas camadas mais pelíticas.Palavras-chave: Modelo Sólido Digital; Travertino; GPR; Laser Scanner terrestre. AbstractMethodological procedures adopted in the digital imagery process, in a non-conventional scale, of a block of travertine rock from the quaternary age were presented on this paper. The rock, named T-Block, weighting 21.2 ton and measuring 1.60 x 1.60 x 2.70 m, was stored in the courtyard of the Laboratório Experimental de Petróleo "Kelsen Valente" (LabPetro) at the Universidade Estadual de Campinas (UNICAMP). Petrobras, the Brazilian Petroleum Company, had sponsored its shipment from Italy to Brazil in order to perform geological studies and petrophysical essays, mainly by research groups from universities and research centers of Brazil that work in the areas of reservoir characterization and 3D digital imaging. The purpose of this work was to develop a Digital Solid Model involving 3D digital imaging techniques of internal and external surfaces of the T-Block. Light Detection and Range technology and Ground Penetrating Radar were used to capture the imaging of the external and internal surfaces, respectively. Additionally, gamma ray profiles were generated by using a portable gamma-spectrometer. The use of Light Detection and Range technology c...

show abstract

Section: Band Pass Frequencyunclassified

Imageamento digital e modelagem virtual 3D de um bloco de rocha travertina

Silva

Castro

Souza

et al. 2014

Geol. USP, Sér. cient.

View full text Add to dashboard Cite

show abstract

“…Basically, two general criteria have been considered in practice: maximum likelihood (ML) and least mean squares error (LMSE), which leads to two alternative solutions, namely (2) where p (z/y) is the joint probability density of the unknown values z conditioned to the known values y and E [z/y] is the conditional mean.…”

Section: Interpolation Methodsmentioning

confidence: 99%

“…A possible solution not requiring reacquisition of the GPR signals is the use of interpolation methods, which can recover missing data from available data. The interpolation of missing or corrupted traces is a common processing step in ground penetrating radar interpretation [2]. This study is limited to the interpolation of GPR B-scans.…”

Section: Introductionmentioning

confidence: 99%

On Recovering Missing Ground Penetrating Radar Traces by Statistical Interpolation Methods

et al. 2014

View full text Add to dashboard Cite

Missing traces in ground penetrating radar (GPR) B-scans (radargrams) may appear because of limited scanning resolution, failures during the acquisition process or the lack of accessibility to some areas under test. Four statistical interpolation methods for recovering these missing traces are compared in this paper: Kriging, Wiener structures, Splines and the expectation assuming an independent component analyzers mixture model (E-ICAMM). Kriging is an adaptation to the spatial context of the linear least mean squared error estimator. Wiener structures improve the linear estimator by including a nonlinear scalar function. Splines are a commonly used method to interpolate GPR traces. This consists of piecewise-defined polynomial curves that are smooth at the connections (or knots) between pieces. E-ICAMM is a new method proposed in this paper. E-ICAMM consists of computing the optimum nonlinear estimator (the conditional mean) assuming a non-Gaussian mixture model for the joint probability density in the observation space. The proposed methods were tested on a set of simulated data and a set of real data, and four performance indicators were computed. Real data were obtained by GPR inspection of two replicas of historical walls. Results show the superiority of E-ICAMM in comparison with the other three methods in the application of reconstructing incomplete B-scans.

show abstract

“…There is a detailed description of GPR and its applications in [68,43], but a brief explanation is given here to make the chapter more self-contained. The description here will focus on GPR working in the time domain, which is more common than those that work in the frequency domain (stepped-frequency systems).…”

Section: Ground-penetrating Radarmentioning

confidence: 99%

“…In the second group of projects, the non-Gaussian mixtures are used to reconstruct damaged data or estimate data at uncaptured positions, thus increasing the capabilities of subsequent methods. Missing data is an important problem in several nondestructive testing applications (e.g., in GPR [43]), and these techniques could help to improve data.…”

Section: Scopementioning

confidence: 99%

New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods

Armero¹

View full text Add to dashboard Cite

This thesis considers new applications of non-Gaussian mixtures in the framework of statistical signal processing and pattern recognition. The non-Gaussian mixtures were implemented by mixtures of independent component analyzers (ICA). The fundamental hypothesis of ICA is that the observed signals can be expressed as a linear transformation of a set of hidden variables, usually referred to as sources, which are statistically independent. This independence allows factoring the original M-dimensional probability density function (PDF) of the data as a product of one-dimensional probability densities, greatly simplifying the modeling of the data. ICA mixture models (ICAMM) provide further flexibility by alleviating the independency requirement of ICA, thus allowing the model to obtain local projections of the data without compromising its generalization capabilities. Here are explored new possibilities of ICAMM for the purposes of estimation and classification of signals.The thesis makes several contributions to the research in non-Gaussian mixtures: (i) a method for maximum-likelihood estimation of missing data, based on the maximization of the PDF of the data given the ICAMM; (ii) a method for Bayesian estimation of missing data that minimizes the mean squared error and can obtain the confidence interval of the prediction; (iii) a generalization of the sequential dependence model for ICAMM to semi-supervised or supervised learning and multiple chains of dependence, thus allowing the use of multimodal data; and (iv) introduction of ICAMM in diverse novel applications, both for estimation and for classification.The developed methods were validated via an extensive number of simulations that covered multiple scenarios. These tested the sensitivity of the proposed methods with respect to the following parameters: number of values to estimate; kinds of source distributions; correspondence of the data with respect to the assumptions of the model; number of classes in the mixture model; and unsupervised, semi-supervised, and supervised learning. The performance of the proposed methods was evaluated using several figures of merit, and compared with the performance of multiple classical and state-of-the-art techniques for estimation and classification.Aside from the simulations, the methods were also tested on several sets of real data from different types: data from seismic exploration studies; ground penetrating radar surveys; and biomedical data. These data correspond to the following applications: reconstruction of damaged or missing data from ground-penetrating radar surveys of historical walls; reconstruction of damaged or missing data from a seismic exploration survey; reconstruction of artifacted or missing electroencephalographic (EEG) data; diagnosis of sleep disorders; modeling of the brain response during memory tasks; and exploration of EEG data from subjects performing a battery of neuropsychological tests. The obtained results demonstrate the capability of the proposed methods to work on problems wi...

show abstract

Ground Penetrating Radar Data Processing, Modelling and Analysis

Cited by 143 publications

References 50 publications

Imageamento digital e modelagem virtual 3D de um bloco de rocha travertina

Imageamento digital e modelagem virtual 3D de um bloco de rocha travertina

On Recovering Missing Ground Penetrating Radar Traces by Statistical Interpolation Methods

New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods

Contact Info

Product

Resources

About