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4. Seismic emissions from a passing train: turning ambient noise into a controlled source

Author

Théo Rebert, Thibaut Allemand, Thomas Bardainne, Caifang Cai, and Hervé Chauris

Abstract

Train traffic is a powerful source of seismic vibrations. Recent studies have shown that trains illuminate geological structures both at the crustal and the geotechnical scale. Existing works have been able to reconstruct approximately the spectral characteristics of the wavefield emitted by a passing train. In this work, we show that we can recover information on the train itself with high accuracy by looking only at the seismic recordings.We record passing trains with seismic accelerometers less than 2 meters away from the track. We can isolate the signal emitted by each wheel, and thus reconstruct the trajectory of the train. This trajectory reconstruction is performed using a non-linear waveform inversion algorithm involving the varying train speed, the spacing between the wheels and an apparent wavelet emitted when the wheel hits close to the seismic sensor. After low-pass filtering the data below 15 Hz for passenger trains passing at around 100 km/h, we obtain harmonious waveforms suitable for our inversion technique. Especially, we are able to pick each wheel from the raw trace, which allows for a robust initial model avoiding local minima trapping during the non-linear inversion. The estimated parameters are minimally influenced by seismic wave propagation speeds, because the closest sleeper dominates the signal in this frequency band.These results suggest that train traffic is a repeatable seismic source that can be can be characterized with good accuracy. By having a better information about the source process, it might be possible to extract more information from the noise recordings, and thus gain in resolution in the imaging of the near surface. Especially, we expect enhanced repeatability of Rayleigh velocities measurements which is important for subsurface monitoring. Further, this also allows for railway traffic monitoring as trains can be identified and their speed measured as they cross seismic arrays.

Published
2023
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7. Reconstruction de fissures 2D à partir d’images courants de Foucault utilisant un modèle direct semi-analytique

Author

Thierry Bore, Eric Vourc'H, Romain Soulat, Caifang Cai, Systèmes et Applications des Technologies de l'Information et de l'Energie (SATIE), École normale supérieure - Cachan (ENS Cachan)-Université Paris-Sud - Paris 11 (UP11)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-École normale supérieure - Rennes (ENS Rennes)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire Spécification et Vérification [Cachan] (LSV), École normale supérieure - Cachan (ENS Cachan)-Centre National de la Recherche Scientifique (CNRS), Université Paris-Seine-Université Paris-Seine-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Centre National de la Recherche Scientifique (CNRS), and Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA))

Subjects
Surface (mathematics), Synthetic data, [SPI.TRON]Engineering Sciences [physics]/Electronics, Image (mathematics), law.invention, law, Genetic algorithm, Eddy current, Current (fluid), Engineering (miscellaneous), Instrumentation, Algorithm, ComputingMilieux_MISCELLANEOUS, Physics::Atmospheric and Oceanic Physics, Excitation, Geology
Abstract

We propose a method for reconstructing 2D surface cracks in electrically conducting parts from Eddy-current images. The proposed method relies on the use of a direct semi-analytic model suitable for Eddy-current systems featuring a uniform current excitation. The surface crack reconstruction approach is based on the comparison of eddy current images computed by the model with the eddy current image of the crack to reconstruct. The method is implemented by means of a genetic algorithm and accurate reconstruction is carried out with synthetic data.

Published
2016
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8. Metamodel-based Markov-Chain-Monte-Carlo parameter inversion applied in eddy current flaw characterization

Author

Dominique Lesselier, Marc Lambert, Caifang Cai, Pierre-Emile Lhuillier, Thomas Rodet, Roberto Miorelli, Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Simulation et de Modélisation Électromagnetique (LSME), Département Imagerie et Simulation pour le Contrôle (DISC), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Laboratoire Génie électrique et électronique de Paris (GeePs), Université Paris-Sud - Paris 11 (UP11)-Université Pierre et Marie Curie - Paris 6 (UPMC)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Systèmes et Applications des Technologies de l'Information et de l'Energie (SATIE), École normale supérieure - Cachan (ENS Cachan)-Université Paris-Sud - Paris 11 (UP11)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-École normale supérieure - Rennes (ENS Rennes)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Centre National de la Recherche Scientifique (CNRS), Matériaux et Mécanique des Composants (EDF R&D MMC), EDF R&D (EDF R&D), EDF (EDF)-EDF (EDF), ANR-13-MONU-0011,ByPASS,Méthodes Bayesiennes pour le diagnostic et la Probabilité de détection Assistée par la Simulation(2013), Laboratoire des signaux et systèmes ( L2S ), Université Paris-Sud - Paris 11 ( UP11 ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de Simulation et de Modélisation Électromagnetique ( LSME ), Département Imagerie et Simulation pour le Contrôle ( DISC ), Laboratoire d'Intégration des Systèmes et des Technologies ( LIST ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies ( LIST ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay, Laboratoire Génie électrique et électronique de Paris ( GeePs ), Centre National de la Recherche Scientifique ( CNRS ) -CentraleSupélec-Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Université Paris-Sud - Paris 11 ( UP11 ), Systèmes et Applications des Technologies de l'Information et de l'Energie ( SATIE ), Centre National de la Recherche Scientifique ( CNRS ) -Conservatoire National des Arts et Métiers [CNAM] ( CNAM ) -Université de Cergy Pontoise ( UCP ), Université Paris-Seine-Université Paris-Seine-École normale supérieure - Rennes ( ENS Rennes ) -Université Paris-Sud - Paris 11 ( UP11 ) -École normale supérieure - Cachan ( ENS Cachan ) -Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux ( IFSTTAR ), EDF - R&D Department MMC and MAI, EDF R&D ( EDF R&D ), EDF ( EDF ) -EDF ( EDF ), ANR-13-MONU-0011,ByPASS,Méthodes Bayesiennes pour le diagnostic et la Probabilité de détection Assistée par la Simulation ( 2013 ), Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Université Paris-Seine-Université Paris-Seine-Conservatoire National des Arts et Métiers [CNAM] (CNAM), and HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
MCMC, Computer science, Bayesian probability, flaw characterization, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, 01 natural sciences, Bayesian, Standard deviation, law.invention, 010104 statistics & probability, symbols.namesake, law, [MATH.MATH-MP]Mathematics [math]/Mathematical Physics [math-ph], Eddy-current testing, Nondestructive testing, 0103 physical sciences, Eddy current, General Materials Science, 0101 mathematics, 010302 applied physics, metamodel, business.industry, Mechanical Engineering, [ MATH.MATH-MP ] Mathematics [math]/Mathematical Physics [math-ph], Inversion, non-destructive testing, Inversion (meteorology), Markov chain Monte Carlo, Condensed Matter Physics, eddy-current, Metamodeling, [SPI.ELEC]Engineering Sciences [physics]/Electromagnetism, [ SPI.ELEC ] Engineering Sciences [physics]/Electromagnetism, symbols, business, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Flaw characterization in eddy current testing usually requires to solve a nonlinear inverse problem. Due to high computational cost, Markov Chain Monte Carlo (MCMC) methods are hardly employed since often needing many forward evaluations. However, they have good potential in dealing with complicated forward models and they do not reduce to only providing the parameters sought. Here, we introduce a computationally-cheap surrogate forward model into a MCMC algorithm for eddy current flaw characterization. Due to the use of a database trained off-line, we benefit from the MCMC algorithm for getting more information and we do not suffer from the computational burden. Numerous experiments are carried out to validate the approach. The results include not only the estimated parameters, but also standard deviations, marginal densities and correlation coefficients between two parameters of interest.

Published
2018
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9. 3D reconstruction of surface cracks using bi-frequency eddy current images and a direct semi-analytic model

Author

Eric Vourc'H, Nicolas Gasnier, Caifang Cai, Florentin Delaine, Thierry Bore, Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), University of Queensland [Brisbane], Systèmes et Applications des Technologies de l'Information et de l'Energie (SATIE), École normale supérieure - Cachan (ENS Cachan)-Université Paris-Sud - Paris 11 (UP11)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-École normale supérieure - Rennes (ENS Rennes)-Université de Cergy Pontoise (UCP), and Université Paris-Seine-Université Paris-Seine-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Surface (mathematics), History, Computer science, Computation, 020208 electrical & electronic engineering, Analytic model, 3D reconstruction, 02 engineering and technology, Inverse problem, 01 natural sciences, Reconstruction method, Computer Science Applications, Education, law.invention, [SPI.TRON]Engineering Sciences [physics]/Electronics, law, 0103 physical sciences, Genetic algorithm, 0202 electrical engineering, electronic engineering, information engineering, Eddy current, 010301 acoustics, Algorithm, ComputingMilieux_MISCELLANEOUS
Abstract

We propose a method for reconstructing 3D surface cracks in metallic parts form eddy current (EC) images. To do so, we use a semi-analytic direct model for representing the interactions between EC and a crack. In order to cope with the inverse problem cons isting in reconstructing 3D cracks, we propose the use of a genetic algorithm based on the computation of bi-frequency EC images. Numerical experiments are carried out in order to analyze the performance of the reconstruction method for different signal to noise ratios. Moreover, the results a bi-frequency genetic algorithm are compared to those a mono-frequency algorithm.

Published
2017
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10. Metamodel-based nested sampling for model selection in eddy-current testing

Author

Sandor Bilicz, Dominique Lesselier, Marc Lambert, Caifang Cai, Thomas Rodet, Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Budapest University of Technology and Economics [Budapest] (BME), Systèmes et Applications des Technologies de l'Information et de l'Energie (SATIE), École normale supérieure - Cachan (ENS Cachan)-Université Paris-Sud - Paris 11 (UP11)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-École normale supérieure - Rennes (ENS Rennes)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Génie électrique et électronique de Paris (GeePs), Université Paris-Sud - Paris 11 (UP11)-Université Pierre et Marie Curie - Paris 6 (UPMC)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), ANR-13-MONU-0011,ByPASS,Méthodes Bayesiennes pour le diagnostic et la Probabilité de détection Assistée par la Simulation(2013), Université Paris-Seine-Université Paris-Seine-Conservatoire National des Arts et Métiers [CNAM] (CNAM), and HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Mathematical optimization, model selection, MCMC, Computer science, 01 natural sciences, Index Terms Nested Sampling, symbols.namesake, Surrogate model, Numerical approximation, [MATH.MATH-MP]Mathematics [math]/Mathematical Physics [math-ph], 0103 physical sciences, Electrical and Electronic Engineering, statistical inversion, surrogate model, 010303 astronomy & astrophysics, Nested sampling algorithm, 010302 applied physics, metamodel, Model selection, Approximation algorithm, non-destructive testing, Markov chain Monte Carlo, Statistical model, marginal likelihood, Marginal likelihood, eddy-current, Electronic, Optical and Magnetic Materials, Metamodeling, [SPI.ELEC]Engineering Sciences [physics]/Electromagnetism, symbols, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

In non-destructive testing, model selection is a common problem, e.g., to determine the number of defects present in the inspected workpiece. Statistical model selection requires to approximate the marginal likelihood also called model evidence. Its numerical approximation is usually computationally expensive. Nested sampling (NS) offers a good compromise between estimation accuracy and computational cost. But, it requires to evaluate the forward model many times. Here, we first propose a general framework where data-fitting surrogate models are used to accelerate the computation. Then, improvements benefiting from surrogate modeling are introduced into the traditional NS algorithm to further reduce the computational cost. These improvements include the use of a sparse-grid surrogate model to deal with the “curse-of-dimensionality” in large dimensional problems and of the preestimated posterior space to save warming-up time. Based on eddy-current simulations, we show that this improved model selection approach has high model selection ability and can jointly perform model selection and parameter inversion.

Published
2017
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11. 3D reconstruction of surface cracks using bi-frequency eddy current images and a direct semi-analytic model

Author

Caifang Cai, Thierry Bore, Florentin Delaine, Nicolas Gasnier, Romain Soulat, Eric Vourc'H, Vourc'H, Eric, Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), University of Queensland [Brisbane], Systèmes et Applications des Technologies de l'Information et de l'Energie (SATIE), École normale supérieure - Cachan (ENS Cachan)-Université Paris-Sud - Paris 11 (UP11)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-École normale supérieure - Rennes (ENS Rennes)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Spécification et Vérification [Cachan] (LSV), and École normale supérieure - Cachan (ENS Cachan)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SPI.ELEC]Engineering Sciences [physics]/Electromagnetism, [SPI.ELEC] Engineering Sciences [physics]/Electromagnetism, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS, [SPI.TRON] Engineering Sciences [physics]/Electronics, [SPI.TRON]Engineering Sciences [physics]/Electronics, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience

Published
2017

12. On a Bayesian inversion approach in eddy-current testing

Author

Caifang Cai, Marc Lambert, Thomas Rodet, Dominique Lesselier, Lesselier, Dominique, Modèles Numériques - Méthodes Bayesiennes pour le diagnostic et la Probabilité de détection Assistée par la Simulation - - ByPASS2013 - ANR-13-MONU-0011 - MN - VALID, H. G. Ramos and A. L. Ribeiro, Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Laboratoire Génie électrique et électronique de Paris (GeePs), Université Paris-Sud - Paris 11 (UP11)-Université Pierre et Marie Curie - Paris 6 (UPMC)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Systèmes et Applications des Technologies de l'Information et de l'Energie (SATIE), École normale supérieure - Cachan (ENS Cachan)-Université Paris-Sud - Paris 11 (UP11)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-École normale supérieure - Rennes (ENS Rennes)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Centre National de la Recherche Scientifique (CNRS), ANR-13-MONU-0011,ByPASS,Méthodes Bayesiennes pour le diagnostic et la Probabilité de détection Assistée par la Simulation(2013), Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris-Sud - Paris 11 (UP11), and Université Paris-Seine-Université Paris-Seine-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SPI.ELEC]Engineering Sciences [physics]/Electromagnetism, [MATH.MATH-MP]Mathematics [math]/Mathematical Physics [math-ph], Bayesian parameter inversion, Benchmarks, [SPI.ELEC] Engineering Sciences [physics]/Electromagnetism, [MATH.MATH-MP] Mathematics [math]/Mathematical Physics [math-ph], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Non-destructive evaluation/testing, Eddy current modeling, Experimental data analysis, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; In eddy-current nondestructive testing (EC-NdT), parameter inversion is one main challenge. Here, one attempts to depict key advances and to put those in perspective with respect to challenges ahead in the real world of EC-NdT within a Bayesian framework. This work is based on the use of the well-known CIVA platform, to compute the variations of eddy-current impedance observed above work pieces that will serve us as synthetic data in the process, and also on the use of databases, those being properly constructed from CIVA, since we are keen to use meta-models (see later) that enable us to avoid in particular being burdened by possibly very high computational costs if left to call for CIVA solvers repeatedly. Data acquired in laboratory-controlled conditions, be they benchmarks or experimental data from partners, are also exploited, that is, they serve us as inputs of parameter inversion, in order to provide further means of analysis of pros and cons of the proposed solution. The work, which will be illustrated by ample numerical simulations and thorough controlled-laboratory data exploitation shown during the expected presentation, is as indicated being led within a Bayesian framework.

Published
2016

13. Surface crack reconstruction from eddy current images using a direct semi-analytic model

Author

Romain Soulat, Eric Vourc'H, Caifang Cai, Thierry Bore, Systèmes et Applications des Technologies de l'Information et de l'Energie (SATIE), École normale supérieure - Rennes (ENS Rennes)-Université Paris-Sud - Paris 11 (UP11)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Cachan (ENS Cachan)-Université Gustave Eiffel (UNIV GUSTAVE EIFFEL), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Laboratoire Spécification et Vérification [Cachan] (LSV), École normale supérieure - Cachan (ENS Cachan)-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Cachan (ENS Cachan)-Université Paris-Sud - Paris 11 (UP11)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-École normale supérieure - Rennes (ENS Rennes)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des signaux et systèmes (L2S), and Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

Subjects
Surface (mathematics), History, Engineering, Flow (psychology), [PHYS.MPHY]Physics [physics]/Mathematical Physics [math-ph], eddy current, Education, law.invention, law, Nondestructive testing, Genetic algorithm, Convergence (routing), Electronic engineering, Eddy current, genetic algorithm, Sensitivity (control systems), ComputingMilieux_MISCELLANEOUS, nondestructive testing, business.industry, imaging, crack retrieval, Computer Science Applications, [SPI.TRON]Engineering Sciences [physics]/Electronics, [SPI.ELEC]Engineering Sciences [physics]/Electromagnetism, business, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Excitation
Abstract

International audience; A method relying on a direct semi-analytic model is proposed for reconstructing cracks from eddy current images. We consider systems featuring a uniform excitation flow which can be modelled by means of fictitious current sources distributed in the crack volume. Thanks to the relative simplicity of the model a reconstruction method based the comparison of EC images is considered. The sensitivity of different images comparison criteria is studied for 2D surface cracks, leading to a reconstruction method based on a genetic algorithm. Numerical experiments are carried out to examine the performances of the algorithm in terms of convergence and accuracy.

Published
2015
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14. Efficient model choice and parameter estimation by using Nested Sampling applied in Eddy-Current Testing

Author

Marc Lambert, Caifang Cai, Thomas Rodet, Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Systèmes et Applications des Technologies de l'Information et de l'Energie (SATIE), École normale supérieure - Cachan (ENS Cachan)-Université Paris-Sud - Paris 11 (UP11)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-École normale supérieure - Rennes (ENS Rennes)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Génie électrique et électronique de Paris (GeePs), Université Paris-Sud - Paris 11 (UP11)-Université Pierre et Marie Curie - Paris 6 (UPMC)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), IEEE, ANR-13-MONU-0011,ByPASS,Méthodes Bayesiennes pour le diagnostic et la Probabilité de détection Assistée par la Simulation(2013), Université Paris-Seine-Université Paris-Seine-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris-Sud - Paris 11 (UP11)

Subjects
computational modeling, Mathematical optimization, Estimation theory, Value (computer science), Sampling (statistics), Sample (statistics), nested sampling, Constraint (information theory), [SPI.ELEC]Engineering Sciences [physics]/Electromagnetism, model choice, [MATH.MATH-MP]Mathematics [math]/Mathematical Physics [math-ph], Eddy-current testing, Key (cryptography), parameter inversion, eddy-current testing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Nested sampling algorithm, Mathematics
Abstract

International audience; In many applications, such as Eddy-Current Testing (ECT), we are often interested in the joint model choice and parameter estimation. Nested Sampling (NS) is one of the possible methods. The key step that reflects the efficiency of the NS algorithm is how to get samples with hard constraint on the likelihood value. This contribution is based on the classical idea where the new sample is drawn within a hyper-ellipsoid, the latter being located from Gaussian approximation. This sampling strategy can automatically guarantee the hard constraint on the likelihood. Meanwhile, it shows the best sampling efficiency for models which have Gaussian-like likelihood distributions. We apply this method in ECT. The simulation results show that this method has high model choice ability and good parameter estimation accuracy, and low computational cost meanwhile.

Published
2015
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15. A full-spectral Bayesian reconstruction approach based on the material decomposition model applied in dual-energy computed tomography

Author

Caifang, Cai, Rodet, Thomas, Legoupil, Samuel, Mohammad-Djafari, Ali, Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Département Imagerie et Simulation pour le Contrôle (DISC), Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), and Laboratoire d'Intégration des Systèmes et des Technologies (LIST)

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; PURPOSE:Dual-energy computed tomography (DECT) makes it possible to get two fractions of basis materials without segmentation. One is the soft-tissue equivalent water fraction and the other is the hard-matter equivalent bone fraction. Practical DECT measurements are usually obtained with polychromatic x-ray beams. Existing reconstruction approaches based on linear forward models without counting the beam polychromaticity fail to estimate the correct decomposition fractions and result in beam-hardening artifacts (BHA). The existing BHA correction approaches either need to refer to calibration measurements or suffer from the noise amplification caused by the negative-log preprocessing and the ill-conditioned water and bone separation problem. To overcome these problems, statistical DECT reconstruction approaches based on nonlinear forward models counting the beam polychromaticity show great potential for giving accurate fraction images.METHODS:This work proposes a full-spectral Bayesian reconstruction approach which allows the reconstruction of high quality fraction images from ordinary polychromatic measurements. This approach is based on a Gaussian noise model with unknown variance assigned directly to the projections without taking negative-log. Referring to Bayesian inferences, the decomposition fractions and observation variance are estimated by using the joint maximum a posteriori (MAP) estimation method. Subject to an adaptive prior model assigned to the variance, the joint estimation problem is then simplified into a single estimation problem. It transforms the joint MAP estimation problem into a minimization problem with a nonquadratic cost function. To solve it, the use of a monotone conjugate gradient algorithm with suboptimal descent steps is proposed.RESULTS:The performance of the proposed approach is analyzed with both simulated and experimental data. The results show that the proposed Bayesian approach is robust to noise and materials. It is also necessary to have the accurate spectrum information about the source-detector system. When dealing with experimental data, the spectrum can be predicted by a Monte Carlo simulator. For the materials between water and bone, less than 5% separation errors are observed on the estimated decomposition fractions.CONCLUSIONS:The proposed approach is a statistical reconstruction approach based on a nonlinear forward model counting the full beam polychromaticity and applied directly to the projections without taking negative-log. Compared to the approaches based on linear forward models and the BHA correction approaches, it has advantages in noise robustness and reconstruction accuracy.

Published
2013
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16. Bayesian data fusion and inversion in X-ray multi-energy computed tomography

Author

Thomas Rodet, Caifang Cai, All Mohammad-Djafari, Samuel Legoupil, Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA))

Subjects
Bayes estimator, Image fusion, business.industry, Attenuation, Bayesian probability, Iterative reconstruction, Inverse problem, Sensor fusion, 01 natural sciences, 030218 nuclear medicine & medical imaging, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Computer vision, Artificial intelligence, 0101 mathematics, Linear combination, business, Algorithm, ComputingMilieux_MISCELLANEOUS, Mathematics
Abstract

In this paper, we first introduce a Multi-Energy Computed Tomography (MECT) forward projection model based on the base material decomposition method. In this method, an object is considered into a linear combination of the fractions of several base materials weighted by X-ray beam energy functions. Then, three different data fusion inversion approaches are proposed to reconstruct the base material fractions. For the first pre-separation reconstruction approach, the base material decomposition is carried on in the projection space while in the second post-separation approach, the base material decomposition is carried on the attenuation coefficients. The third approach is a joint Bayesian inversion method. Finally, the reconstruction performances of the three reconstruction methods are compared on the simulated data.

Published
2011
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17. Joint reduce of metal and beam hardening artifacts using Multi-Energy map approach in X-ray Computed Tomography

Author

Thomas Rodet, Caifang Cai, and Yuling Zheng

Subjects
medicine.diagnostic_test, Computer science, Iterative method, business.industry, Gaussian, Detector, Computed tomography, Reconstruction algorithm, Iterative reconstruction, symbols.namesake, Metal Artifact, symbols, medicine, Computer vision, Tomography, Artificial intelligence, business, Energy (signal processing)
Abstract

Metal and beam-hardening artifacts are tough issues in Computed Tomography (CT) images. This paper proposes an iterative Maximum A Posteriori (MAP) reconstruction algorithm aiming to reduce both of them. This algorithm is based on a multi-energy acquisition system, a Gaussian noised measuring model and a basis material decomposition formula. In the Multi-Energy Computed Tomography (MECT) system, an energy discriminant detector which can measure the flux of photons at different energies is supposed to be employed. Our method can reconstruct two separate base material density maps where the metal and beam-hardening artifacts are highly reduced.

Published
2011
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18. A full-spectral Bayesian reconstruction approach based on the material decomposition model applied in dual-energy computed tomography

Author

Ali Mohammad-Djafari, Caifang Cai, Samuel Legoupil, and Thomas Rodet

Subjects
Mathematical optimization, Monte Carlo method, Linear model, General Medicine, Iterative reconstruction, 030218 nuclear medicine & medical imaging, Normal distribution, 03 medical and health sciences, symbols.namesake, Noise, 0302 clinical medicine, Gaussian noise, 030220 oncology & carcinogenesis, Conjugate gradient method, symbols, Maximum a posteriori estimation, Algorithm, Mathematics
Abstract

Purpose: Dual-energy computed tomography (DECT) makes it possible to get two fractions of basis materials without segmentation. One is the soft-tissue equivalent water fraction and the other is the hard-matter equivalent bone fraction. Practical DECT measurements are usually obtained with polychromatic x-ray beams. Existing reconstruction approaches based on linear forward models without counting the beam polychromaticity fail to estimate the correct decomposition fractions and result in beam-hardening artifacts (BHA). The existing BHA correction approaches either need to refer to calibration measurements or suffer from the noise amplification caused by the negative-log preprocessing and the ill-conditioned water and bone separation problem. To overcome these problems, statistical DECT reconstruction approaches based on nonlinear forward models counting the beam polychromaticity show great potential for giving accurate fraction images.Methods: This work proposes a full-spectral Bayesian reconstruction approach which allows the reconstruction of high quality fraction images from ordinary polychromatic measurements. This approach is based on a Gaussian noise model with unknown variance assigned directly to the projections without taking negative-log. Referring to Bayesian inferences, the decomposition fractions and observation variance are estimated by using the joint maximum a posteriori (MAP) estimation method. Subject to an adaptive prior model assigned to the variance, the jointmore » estimation problem is then simplified into a single estimation problem. It transforms the joint MAP estimation problem into a minimization problem with a nonquadratic cost function. To solve it, the use of a monotone conjugate gradient algorithm with suboptimal descent steps is proposed.Results: The performance of the proposed approach is analyzed with both simulated and experimental data. The results show that the proposed Bayesian approach is robust to noise and materials. It is also necessary to have the accurate spectrum information about the source-detector system. When dealing with experimental data, the spectrum can be predicted by a Monte Carlo simulator. For the materials between water and bone, less than 5% separation errors are observed on the estimated decomposition fractions.Conclusions: The proposed approach is a statistical reconstruction approach based on a nonlinear forward model counting the full beam polychromaticity and applied directly to the projections without taking negative-log. Compared to the approaches based on linear forward models and the BHA correction approaches, it has advantages in noise robustness and reconstruction accuracy.« less

Published
2013
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19. Broadband electromagnetic analysis of compacted kaolin.

Author

Thierry Bore, Norman Wagner, Caifang Cai, and Alexander Scheuermann

Subjects
AQUEOUS solutions, POROSITY, KAOLIN, POROUS materials, SOIL compaction
Abstract

The mechanical compaction of soil influences not only the mechanical strength and compressibility but also the hydraulic behavior in terms of hydraulic conductivity and soil suction. At the same time, electric and dielectric parameters are increasingly used to characterize soil and to relate them with mechanic and hydraulic parameters. In the presented study electromagnetic soil properties and suction were measured under defined conditions of standardized compaction tests. The impact of external mechanical stress conditions of nearly pure kaolinite was analyzed on soil suction and broadband electromagnetic soil properties. An experimental procedure was developed and validated to simultaneously determine mechanical, hydraulic and broadband (1 MHz–3 GHz) electromagnetic properties of the porous material. The frequency dependent electromagnetic properties were modeled with a classical mixture equation (advanced Lichtenecker and Rother model, ALRM) and a hydraulic-mechanical-electromagnetic coupling approach was introduced considering water saturation, soil structure (bulk density, porosity), soil suction (pore size distribution, water sorption) as well as electrical conductivity of the aqueous pore solution. Moreover, the relaxation behavior was analyzed with a generalized fractional relaxation model concerning a high-frequency water process and two interface processes extended with an apparent direct current conductivity contribution. The different modeling approaches provide a satisfactory agreement with experimental data for the real part. These results show the potential of broadband electromagnetic approaches for quantitative estimation of the hydraulic state of the soil during densification. [ABSTRACT FROM AUTHOR]

Published
2017
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