Inversion conjointe des données électriques et de radar en forage

RÉSUMÉ Dans le cadre de cette thèse, deux algorithmes d‘inversion conjointe des données électriques et de radar en forage ont été développés. Le premier algorithme combine une approche basée sur l‘échange de l‘information structurale entre deux inversions séparées et une régularisation dans le domaine des ondelettes qui force la solution à avoir une représentation creuse des coefficients en ondelettes. Cette régularisation consiste à appliquer un algorithme de seuillage doux à chaque itération d‘un algorithme de descente. L‘opération de seuillage nécessite le calcul de seuils qui sont déterminés dans notre cas en maximisant un critère de similarité structurale entre les modèles de résistivité et de lenteur. Comme la régularisation dans le domaine des ondelettes permet la reconstruction des discontinuités de contraste fort ainsi que les zones homogène, nous proposons d‘utiliser le détecteur de contours Canny pour extraire l‘information structurale de chaque modèle. Les contours ainsi détectés sont utilisés pour construire des matrices de pondération qui sont appliquées à la matrice de rugosité de chaque inversion séparée. Pour valider cet algorithme trois modèles synthétiques ont été utilisés. Les résultats montrent que celui-ci permet d‘améliorer la résolution spatiale, ainsi qu‘une meilleure estimation des propriétés physiques, en comparaison avec l‘inversion séparée. De plus, il présente l‘avantage d‘être très robuste lorsque le niveau du bruit est élevé. Dans le deuxième algorithme, on propose de combiner une inversion coopérative par zonation et une approche bayésienne hiérarchique. L‘inversion coopérative par zonation consiste à utiliser séquentiellement une approche de classification non-hiérarchique et un algorithme d‘inversion séparée. Dans un processus itératif, l‘algorithme de classification non-hiérarchique est appliqué sur les résultats obtenus par inversion séparée pour générer des modèles composés de plusieurs zones homogènes représentant chacune une certaine lithologie du milieu investigué. Les modèles ainsi construits sont ensuite utilisés comme modèles a priori dans une nouvelle étape d‘inversion séparée. La solution obtenue par une telle approche peut être biaisé vers le modèle a priori qui est fonction du nombre de classes dans l‘algorithme de classification non-hiérarchique.----------ABSTRACT We present two joint structural inversion algorithm for cross-hole electrical resistance tomography (ERT) and cross-hole radar travel time tomography (RTT). The first algorithm proceeds by combining the exchange of structural information and a regularization method that consists of imposing an L1-norm penalty in the wavelet domain. The minimization of the L1-norm penalty is carried out using an iterative soft-thresholding algorithm. The thresholds are estimated by maximizing a structural similarity criterion, which is a function of the two (ERT and RTT) inverted models. Besides, the regularization in the wavelet basis allows for the possibility of sharp discontinuities superimposed on a smoothly varying background. Hence the structural information is extracted from each model using a Canny edge detector. The detected edge serves to construct a weighting matrix that is used to alter the smoothness matrix constraint. To validate our methodology and its implementation, three synthetic models were created. Experiments demonstrate that the proposed approach improves the spatial resolution and quantitative estimation of physical parameters. In addition, it seems to be more robust in high noise level condition. In the second algorithm, we propose to combine a zonal cooperative inversion (ZCI) scheme with a hierarchical Bayesian approach, in order to invert cooperatively cross-hole ERT data and cross-hole radar travel time data. The basic idea of ZCI is to use cooperatively cluster analysis and separate inversion algorithm. For each iteration cluster analysis of separate inversion results is used to construct models that contain the parameter characteristics of dominant subsurface structures. These constructed models are then used as starting model in the next iteration of separate inversion. The resulting models are then biased to starting models which are a function of the number of clusters. To overcome this problem, we formulate the inverse problem within a hierarchical Bayesian framework where the hierarchical prior distribution is based on the a priori models constructed from cluster analysis

A framework for parameter estimation using sharp-interface seawater intrusion models

Funding : This work was supported by Quebec’s Ministère de l'Environnement et de la Lutte contre les changements climatiques (MELCC) [project « Acquisition de connaissances sur les eaux souterraines dans la région des Îles-de-la-Madeleine » (Groundwater characterization project in the Magdalen Islands region)]; and the Fonds québécois de la recherche sur la nature et les technologies (FRQNT) [International internship program accessed through CentrEau, the Quebec Water Research Center]. The authors would like to thank the Municipality of Les Îles-de-la-Madeleine for providing pumping datasets and information on current and historical groundwater management. They would also like to thank the team at Université Laval working on the Magdalen Islands project, for their help acquiring datasets and for field logistics, John Molson, for proofreading, and finally the two anonymous reviewers for their valuable comments. The authors would also like to thank Vincent Post for discussions on deep open boreholes, and Francesca Lotti and John Doherty for discussions on seawater intrusion modeling and data assimilation. J-C Comte and O Banton acknowledge the financial support from the Fonds d'Action Québécois pour le Développement Durable for the ERT data collection, undertaken as part of the Madelin'Eau consortium (Ageos-Enviro'Puits-Hydriad), and further thank the Municipality of Les Îles-de-la-Madeleine for fieldwork logistical and technical support.Peer reviewedproo

Derivation of lowland riparian wetland deposit architecture using geophysical image analysis and interface detection

For groundwater-surface water interactions to be understood in complex wetland settings, the architecture of the underlying deposits requires investigation at a spatial resolution sufficient to characterize significant hydraulic pathways. Discrete intrusive sampling using conventional approaches provides insufficient sample density and can be difficult to deploy on soft ground. Here a noninvasive geophysical imaging approach combining three-dimensional electrical resistivity tomography (ERT) and the novel application of gradient and isosurface-based edge detectors is considered as a means of illuminating wetland deposit architecture. The performance of three edge detectors were compared and evaluated against ground truth data, using a lowland riparian wetland demonstration site. Isosurface-based methods correlated well with intrusive data and were useful for defining the geometries of key geological interfaces (i.e., peat/gravels and gravels/Chalk). The use of gradient detectors approach was unsuccessful, indicating that the assumption that the steepest resistivity gradient coincides with the associated geological interface can be incorrect. These findings are relevant to the application of this approach in settings with a broadly layered geology with strata of contrasting resistivities. In addition, ERT revealed substantial structures in the gravels related to the depositional environment (i.e., braided fluvial system) and a complex distribution of low-permeability putty Chalk at the bedrock surface—with implications for preferential flow and variable exchange between river and groundwater systems. These results demonstrate that a combined approach using ERT and edge detectors can provide valuable information to support targeted monitoring and inform hydrological modeling of wetlands

Traitement des données MEGATEM II

RÉSUMÉ Dans le cadre du projet « valorisation de la technologie MEGATEM », ce travail propose plusieurs techniques de traitement de l'enregistrement brut. Le MEGATEM est un système électromagnétique aéroporté transitoire (ATEM) qui est largement utilisé dans l'industrie minière pour la recherche des gisements métallifères (Cu, Zn, Au, Ag, etc.). II présente l'avantage d'avoir un moment magnétique très important qui permet une grande profondeur d'investigation, allant jusqu'à 250 m. Plus le corps est profond, plus l'anomalie qui lui est associée est faible. Dans un contexte de rapport signal à bruit faible, il serait avantageux de mieux éliminer certains bruits gênants comme les décharges atmosphériques et le 60 Hz. L'objectif de mon travail est double : d'une part, développer une nouvelle méthodologie pour éliminer les décharges atmosphériques et de voir leur utilisation en interprétation. D'autre part, reprendre toute la chaîne de traitement du début jusqu'à la fin. L'élimination des décharges atmosphériques avant sommation présente un avantage évident sur les méthodes d'interpolation qui ne fonctionnent pas généralement en cas d'un nombre important. Ceci est rendu possible par l'analyse multi-résolution qui permet d'analyser un signal à différentes résolutions, agissant comme un « microscope mathématique ». En comprimant les ondelettes, on accroît le grossissement de ce microscope, pour révéler les détails de plus en plus fins et ainsi mettre en évidence le caractère transitoire des décharges atmosphériques, tout en les séparant du signal mile qui lui apparaît sur des détails plus grossiers. Sur ce, une stratégie a été élaborée pour les extraire chirurgicalement. Elle consiste en leur détection sur les coefficients du premier détail, le plus fin, en utilisant un détecteur d'énergie. Ensuite, les coefficients correspondants dans les deux premiers détails sont mis à zéro, puis le signal est reconstruit. Cette approche s'avère être très robuste et fonctionne indifféremment sur les parties du signal où le transmetteur est en marche ou en arrêt, et même dans le cas de la présence de plusieurs d'entre eux. On montre qu'après leur extraction qu'il est possible de les utiliser en interprétation, en calculant le Tipper et ses différents paramètres. En général, La présence des lignes à haute tension pose énormément de problèmes lors des levés électromagnétiques. L'un de leur effet des plus gênant se manifeste par des radiations électromagnétiques aux fréquences de 60 Hz et de ses harmoniques impairs. Leur enregistrement est un signal non stationnaire, ainsi pour les éliminer, on a testé un filtrage adaptatif qui suppose une fréquence, une amplitude et une phase variables avec le temps. Les résultats sont très encourageants. L'établissement d'un schéma de traitement, de l'enregistrement brut au signal prêt à être interprété, a permis de mettre en évidence l'impact de chaque étape, ainsi que de disposer d'une plateforme qui permettra la réalisation d'un logiciel de traitement disponible pour de nouveaux développements et surtout pour les besoins de l'industrie dont la demande ne cesse d'accroître.--------------ABSTRACT Within framework of the project" MEGATEM Technology Enhancement ", this thesis proposes several raw data processing techniques. The MEGATEM is an airborne transient electromagnetic system used extensively in the mining industry for the detection of metalliferous bodies (Cu, Zn, Au, Ag, etc.). It possesses the characteristic of a very large magnetic moment which permits large depths of investigation (250 m). As is known, the deeper the body the weaker the associated anomaly. In the context of weak signal to noise ratios, it would be advantageous to better eliminate bothersome noise such as atmospherics and the 60 Hz transmission line signal. The objectives of my work are : (1) to develop a new methodology to eliminate atmospherics and to see their use in interpretation; (2), to treat the MEGATEM data processing stream in its entirely. The elimination of atmospherics before stacking possesses an obvious advantage over interpolation methods which are not generally successful when many atmospherics are present. Eliminating atmospherics is made possible by multi-resolution wavelet analysis which permits signal analysis at various resolutions, acting like a" mathematical microscope ". While compressing the wavelets, one increases the magnification of this "microscope" to reveal the signal at fine scales details. The transient character of atmospherics is highlighted. The useful signal component appears in the detail signals at the coarse scales. A strategy has been elaborated to extract atmospherics. It consists of their detection in the fast detail coefficients, using an energy detector. Then, the corresponding coefficients in the first two detail signals are set to zero, and the signal is reconstructed. This approach proves to be very robust and is successful regardless of whether the transmitter is on or off, and even in the case where several atmospherics are present. We show that after their extraction, it is possible to use atmospherics in interpretation by calculating the Tipper and its various parameters. In general, the presence of power-lines poses problems in EM surveys. One of their most bothersome effects results from EM radiation at the frequencies of 60 Hz and its harmonics. Which signal is non stationary. To eliminate them, adaptive filtering which calculates a frequency, amplitude and phase variables as function of time is tested. The results are very encouraging. A raw data processing flow chart is created. The flowchart highlights every step necessary for generating geologically interpretable signals. The flow chart serves as a platform for realizing of publically available software processing package for industry whose demands continue to increase.-------------CONTENU Un système AEM : le megatem -- Classification des systèmes AEM -- Principe physique -- Le Megatem -- L'enregistrement -- Traitement des données -- Ce que fait Fugro -- Ce qu'on propose -- Comparaison -- Caractérisation et extraction des atmosphériques -- Définition et caractéristiques du signal AFMAG -- Les enregistrements -- Analyse spectral -- Occurrence et intensité des atmosphériques -- Revue de quelques travaux sur les AT -- Conclusion sur l'énergie des AT -- Élimination des AT par ondelettes -- Les avantages et les limitations de l'approche par l'analyse en ondelettes discrètes -- Interprétation AFMAG -- Le tipper -- La validation de l'interprétation AFMAG -- Avantages et limitations

Three-dimensional stochastic assimilation of gravity data in Lalor volcanogenic massive sulphide, Manitoba, Canada

We propose a new numerical workflow based on stochastic data integration where we merge a conceptual geological model, the drillhole geophysical and geological logs as well as surface geophysical data to compute a unified numerical model of a VMS deposit. The first step of the workflow consists in building a 3D numerical conceptual model of the geology. This conceptual model as well as geological logs are then used to generate multiple equiprobable scenarios of the geology by means of multiple point simulation (MPS). The MPS method studies high-order statistics in the space of a numerical conceptual model making it possible to reproduce complex geological structures. We then use conventional conditional sequential Gaussian simulation, which is a method based on a node-by-node sequential process to stochastically populate the geological grid with densities. For this purpose we use available density logs to simulate multiple equiprobable spatial distributions of the density at high spatial resolution within each geological unit separately. The stochastic high-resolution density models are iteratively combined by the gradual deformation method in order to minimize the difference between measured Bouguer anomaly data and the data computed on the combined realizations of density. Application of the proposed method to the Lalor deposit, a volcanogenic massive sulphide deposit in Manitoba, Canada, produces a density model that honours the geology of the deposit and the Bouguer anomaly data. This unified model has the advantage to include all the available information (geological and density logs and surface geophysics) at scales appropriate for mining applications.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

2-D joint structural inversion of cross-hole electrical resistance and ground penetrating radar data

We present a joint structural inversion algorithm for cross-hole electrical resistance tomography (ERT) and cross-hole radar travel time tomography (RTT) that encourages coincident sharp changes on a smoothly varying background in the two models. The proposed approach is based on the combination of two iterative soft-thresholding inversion algorithms in parallel manner where the structural information is exchanged at each iteration. Iterative thresholding algorithm allows to obtain a sparse wavelet representation of the model (blocky model) by applying a thresholding operator to the wavelet coefficients of model obtained through a Gauss-Newton iteration. The structural information is introduced in the inversion system using the smoothness weighting matrices that control boundary cells and the thresholds that are estimated by maximizing a structural similarity criterion, which is a function of the two (ERT and RTT) models. A Canny edge detector is implemented to extract the structural information. The detected edges serve to build a weighting matrix that is used to alter the smoothness matrix constraint. To validate our methodology and its implementation, tests were performed on three synthetic models. The results show that the parameters estimated by our joint inversion approach are more consistent than those from individual inversions and another joint inversion algorithm. In addition, our approach appears to be robust in high noise level conditions. Finally, the proposed algorithm was applied for vadose zone characterisation in a sandstone aquifer. It achieves results that are consistent with hydrogeological information and geophysical logs available at the site. The results were also compared in terms of structural similarities to models obtained by a joint structural inversion algorithm with a cross-gradient constraint. Based on this comparison and hydrogeologic information, we conclude that the proposed algorithm allows to the RTT and ERT models to be dissimilar in the areas where the data are incompatible. (C) 2011 Elsevier B.V. All rights reserved