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

Implantation expérimentale d’un variateur de vitesse intelligent à courant continu

Dans ce travail on a fait une implantation expérimentale d’un variateur de vitesse intelligent à courant continu, l’avantage de ce variateur de vitesse est son aptitude d’adapté avec plusieurs moteurs d’une façon automatique sans d’intervention manuel sur leur paramètres, il doit calculer les paramètres de la régulation de vitesse d’une façon automatique dépend de la valeur de résistance et d’inductance de l’induit du moteur, On a simulé le système de variation de vitesse à base d’un hacheur série et un hacheur série- parallèle sur Simulink Matlab par un schéma block mathématique puis on a simulé aussi sur SimPowerSystems Matlab pour approché plus à la réalité afin de faire une implantation expérimentale de ce variateur de vitesse associer avec un moteur à courant continu et varier la vitesse de ce moteur

Comparison Between Hydraulic Conductivity Anisotropy and Electrical Resistivity Anisotropy From Tomography Inverse Modeling.

Hydrogeophysics is increasingly used to understand groundwater flow and contaminant transport, essential basis for groundwater resources forecast, management, and remediation. It has proven its ability to improve the characterization of the hydraulic conductivity (K) when used along with hydrogeological knowledge. Geophysical tools and methods provide high density information of the spatial distribution of physical properties in the ground at relatively low costs and in a non-destructive manner. Amongst them, the Electrical Resistivity Tomography (ERT) has been widely used for its high spatial coverage and for the strong theoretical links between electrical resistivity (ρ) and key hydrogeological parameters, such as K. Historically, ERT data processing was based on isotropic hypothesis. However, the unconsolidated aquifers in Canada reveal in most cases a strong anisotropic behavior for K both with in situ or laboratory measurements. Recently, electrical anisotropy has been considered model-wise, but it is seldom considered as an interpretation tool or in the characterization process of the anisotropy of K. In order to evaluate the potential of ERT to assess the anisotropy of electrical resistivity, we developed a forward and inverse modeling code. These codes have been validated and tested on a realistic synthetic case reproducing the behavior of a real aquifer extensively characterized, the site of Saint-Lambert-de-Lauzon in Quebec (Canada). On this site, innovative in situ hydraulic tomography has revealed a strong anisotropy, with up to three orders of magnitude between horizontal and vertical K components. In order to confirm the link between in situ K- and ρ-anisotropies, an ERT survey has been performed, using the same wells as for the hydraulic tomography. The inversion confirms a strong link between K- and ρ-anisotropies. It demonstrates the suitability of the anisotropic ERT approach coupled with well measurements to provide better estimates of K and its anisotropy at the scale of a site

Aim4res, an open-source 2.5D finite differences MATLAB library for anisotropic electrical resistivity modeling.

Electrical Resistivity Tomography (ERT) is one of the oldest geophysical techniques, and due to the advances of numerical techniques along with computational resources, it is widely used for geophysical prospecting. It has found various domains of application as it is easy to implement and fast to image the ground resistivity heterogeneity. However, anisotropy, which is another key resistivity parameter, is seldom considered. Although being a well-known phenomenon, its consideration in the characterization process is only recent. Amongst the reasons behind this is the absence of available anisotropic resistivity modeling tools. We present aim4res (Anisotropic Inverse Modeling for RESisitivity) to that end. This open-source MATLAB library allows for 2.5D forward and inverse anisotropic resistivity modeling based on a finite differences scheme. The inverse problem is solved with a Gauss–Newton algorithm. The regularization coefficient, initial model and constraints can be adjusted from prior knowledge in order to avoid local minima during optimization. Analytical and synthetic studies have been carried out to prove the reliability of aim4res. The results demonstrate its ability to identify anisotropy, along with the correct geometry and resistivity amplitude. It is also able to correctly detect isotropy, as the inversion comparison with a previous toolbox already proven working showed. A real case study inversion is carried out to demonstrate that aim4res is a relevant tool to use on the field, able to reveal strong anisotropy fields even at short scales

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

Iron Grades Estimation Using Bayesian Sequential Simulation - Lalor Case Study.

This work presents the first step of the use of a Bayesian sequential simulation algorithm to estimate the iron grades of Lalor VMS deposit. The approach is based on an in situ petrophysical relationship between iron grades and both conductivity and density. This statistical relationship is used to interpolate and extrapolate the iron grades over a 3D conductivity and density volume. Furthermore, this stochastic algorithm allows estimating the spatial uncertainty among a set of equiprobable realization

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