Conditioning of stochastic 3-D fracture networks to hydrological and geophysical data

The geometry and connectivity of fractures exert a strong influence on the flow and transport properties of fracture networks. We present a novel approach to stochastically generate three-dimensional discrete networks of connected fractures that are conditioned to hydrological and geophysical data. A hierarchical rejection sampling algorithm is used to draw realizations from the posterior probability density function at different conditioning levels. The method is applied to a well-studied granitic formation using data acquired within two boreholes located 6 m apart. The prior models include 27 fractures with their geometry (position and orientation) bounded by information derived from single-hole ground-penetrating radar (GPR) data acquired during saline tracer tests and optical televiewer logs. Eleven cross-hole hydraulic connections between fractures in neighboring boreholes and the order in which the tracer arrives at different fractures are used for conditioning. Furthermore, the networks are conditioned to the observed relative hydraulic importance of the different hydraulic connections by numerically simulating the flow response. Among the conditioning data considered, constraints on the relative flow contributions were the most effective in determining the variability among the network realizations. Nevertheless, we find that the posterior model space is strongly determined by the imposed prior bounds. Strong prior bounds were derived from GPR measurements and helped to make the approach computationally feasible. We analyze a set of 230 posterior realizations that reproduce all data given their uncertainties assuming the same uniform transmissivity in all fractures. The posterior models provide valuable statistics on length scales and density of connected fractures, as well as their connectivity. In an additional analysis, effective transmissivity estimates of the posterior realizations indicate a strong influence of the DFN structure, in that it induces large variations of equivalent transmissivities between realizations. The transmissivity estimates agree well with previous estimates at the site based on pumping, flowmeter and temperature data.

Use of high-resolution geophysical data to characterize heterogeneous aquifers: influence of data integration method on hydrological predictions

The integration of geophysical data into the subsurface characterization problem has been shown in many cases to significantly improve hydrological knowledge by providing information at spatial scales and locations that is unattainable using conventional hydrological measurement techniques. The investigation of exactly how much benefit can be brought by geophysical data in terms of its effect on hydrological predictions, however, has received considerably less attention in the literature. Here, we examine the potential hydrological benefits brought by a recently introduced simulated annealing (SA) conditional stochastic simulation method designed for the assimilation of diverse hydrogeophysical data sets. We consider the specific case of integrating crosshole ground-penetrating radar (GPR) and borehole porosity log data to characterize the porosity distribution in saturated heterogeneous aquifers. In many cases, porosity is linked to hydraulic conductivity and thus to flow and transport behavior. To perform our evaluation, we first generate a number of synthetic porosity fields exhibiting varying degrees of spatial continuity and structural complexity. Next, we simulate the collection of crosshole GPR data between several boreholes in these fields, and the collection of porosity log data at the borehole locations. The inverted GPR data, together with the porosity logs, are then used to reconstruct the porosity field using the SA-based method, along with a number of other more elementary approaches. Assuming that the grid-cell-scale relationship between porosity and hydraulic conductivity is unique and known, the porosity realizations are then used in groundwater flow and contaminant transport simulations to assess the benefits and limitations of the different approaches.

Integration of high-resolution geophysical data for the characterization of alluvial aquifers : new approaches and implications for hydrological predictions

Abstract Accurate characterization of the spatial distribution of hydrological properties in heterogeneous aquifers at a range of scales is a key prerequisite for reliable modeling of subsurface contaminant transport, and is essential for designing effective and cost-efficient groundwater management and remediation strategies. To this end, high-resolution geophysical methods have shown significant potential to bridge a critical gap in subsurface resolution and coverage between traditional hydrological measurement techniques such as borehole log/core analyses and tracer or pumping tests. An important and still largely unresolved issue, however, is how to best quantitatively integrate geophysical data into a characterization study in order to estimate the spatial distribution of one or more pertinent hydrological parameters, thus improving hydrological predictions. Recognizing the importance of this issue, the aim of the research presented in this thesis was to first develop a strategy for the assimilation of several types of hydrogeophysical data having varying degrees of resolution, subsurface coverage, and sensitivity to the hydrologic parameter of interest. In this regard a novel simulated annealing (SA)-based conditional simulation approach was developed and then tested in its ability to generate realizations of porosity given crosshole ground-penetrating radar (GPR) and neutron porosity log data. This was done successfully for both synthetic and field data sets. A subsequent issue that needed to be addressed involved assessing the potential benefits and implications of the resulting porosity realizations in terms of groundwater flow and contaminant transport. This was investigated synthetically assuming first that the relationship between porosity and hydraulic conductivity was well-defined. Then, the relationship was itself investigated in the context of a calibration procedure using hypothetical tracer test data. Essentially, the relationship best predicting the observed tracer test measurements was determined given the geophysically derived porosity structure. Both of these investigations showed that the SA-based approach, in general, allows much more reliable hydrological predictions than other more elementary techniques considered. Further, the developed calibration procedure was seen to be very effective, even at the scale of tomographic resolution, for predictions of transport. This also held true at locations within the aquifer where only geophysical data were available. This is significant because the acquisition of hydrological tracer test measurements is clearly more complicated and expensive than the acquisition of geophysical measurements. Although the above methodologies were tested using porosity logs and GPR data, the findings are expected to remain valid for a large number of pertinent combinations of geophysical and borehole log data of comparable resolution and sensitivity to the hydrological target parameter. Moreover, the obtained results allow us to have confidence for future developments in integration methodologies for geophysical and hydrological data to improve the 3-D estimation of hydrological properties.

Permeability estimates from poro-elastic dispersion analyses of sonic logs

Modern sonic logging tools designed for shallow environmental and engineering applications allow for P-wave phase velocity measurements over a wide frequency band. Methodological considerations indicate that, for saturated unconsolidated sediments in the silt to sand range and source frequencies ranging from approximately 1 to 30 kHz, the observable poro-elastic P-wave velocity dispersion is sufficiently pronounced to allow for reliable first-order estimations of the underlying permeability structure. These predictions have been tested on and verified for a surficial alluvial aquifer. Our results indicate that, even without any further calibration, the thus obtained permeability estimates as well as their variabilities within the pertinent lithological units are remarkably close to those expected based on the corresponding granulometric characteristics.

Control of landslide retrogression by discontinuities: Evidence by the integration of airbone- and ground-based geophysical information

The objective of this work is to present a multitechnique approach to define the geometry, the kinematics, and the failure mechanism of a retrogressive large landslide (upper part of the La Valette landslide, South French Alps) by the combination of airborne and terrestrial laser scanning data and ground-based seismic tomography data. The advantage of combining different methods is to constrain the geometrical and failure mechanism models by integrating different sources of information. Because of an important point density at the ground surface (4. 1 points m?2), a small laser footprint (0.09 m) and an accurate three-dimensional positioning (0.07 m), airborne laser scanning data are adapted as a source of information to analyze morphological structures at the surface. Seismic tomography surveys (P-wave and S-wave velocities) may highlight the presence of low-seismic-velocity zones that characterize the presence of dense fracture networks at the subsurface. The surface displacements measured from the terrestrial laser scanning data over a period of 2 years (May 2008?May 2010) allow one to quantify the landslide activity at the direct vicinity of the identified discontinuities. An important subsidence of the crown area with an average subsidence rate of 3.07 m?year?1 is determined. The displacement directions indicate that the retrogression is controlled structurally by the preexisting discontinuities. A conceptual structural model is proposed to explain the failure mechanism and the retrogressive evolution of the main scarp. Uphill, the crown area is affected by planar sliding included in a deeper wedge failure system constrained by two preexisting fractures. Downhill, the landslide body acts as a buttress for the upper part. Consequently, the progression of the landslide body downhill allows the development of dip-slope failures, and coherent blocks start sliding along planar discontinuities. The volume of the failed mass in the crown area is estimated at 500,000 m3 with the sloping local base level method.

Combining geophysical and laboratory measurements for civil Engineering.

The study of wave propagation at sonic frequency in soil leads to elasticity parameter determination. These parameters are compatible to those measured simultaneously by static loading. The acquisition of in situ elasticity parameter combined with laboratory description of the elastoplastic behaviour can lead to in situ elastoplastic curves. - L'étude de la propagation des ondes acoustiques permet la détermination des paramètres d'élasticité dans les sols. Ces paramètres sont cohérents avec des mesures statiques simultanées. L'acquisition des paramètres d'élasticité in situ associée à une description du comportement élasto-plastique mesuré en laboratoire permet d'obtenir des courbes d'élastoplasticité in situ.

Quantitative integration of geophysical and hydraulic data : from the local towards the regional scale range

Des progrès significatifs ont été réalisés dans le domaine de l'intégration quantitative des données géophysique et hydrologique l'échelle locale. Cependant, l'extension à de plus grandes échelles des approches correspondantes constitue encore un défi majeur. Il est néanmoins extrêmement important de relever ce défi pour développer des modèles fiables de flux des eaux souterraines et de transport de contaminant. Pour résoudre ce problème, j'ai développé une technique d'intégration des données hydrogéophysiques basée sur une procédure bayésienne de simulation séquentielle en deux étapes. Cette procédure vise des problèmes à plus grande échelle. L'objectif est de simuler la distribution d'un paramètre hydraulique cible à partir, d'une part, de mesures d'un paramètre géophysique pertinent qui couvrent l'espace de manière exhaustive, mais avec une faible résolution (spatiale) et, d'autre part, de mesures locales de très haute résolution des mêmes paramètres géophysique et hydraulique. Pour cela, mon algorithme lie dans un premier temps les données géophysiques de faible et de haute résolution à travers une procédure de réduction déchelle. Les données géophysiques régionales réduites sont ensuite reliées au champ du paramètre hydraulique à haute résolution. J'illustre d'abord l'application de cette nouvelle approche dintégration des données à une base de données synthétiques réaliste. Celle-ci est constituée de mesures de conductivité hydraulique et électrique de haute résolution réalisées dans les mêmes forages ainsi que destimations des conductivités électriques obtenues à partir de mesures de tomographic de résistivité électrique (ERT) sur l'ensemble de l'espace. Ces dernières mesures ont une faible résolution spatiale. La viabilité globale de cette méthode est testée en effectuant les simulations de flux et de transport au travers du modèle original du champ de conductivité hydraulique ainsi que du modèle simulé. Les simulations sont alors comparées. Les résultats obtenus indiquent que la procédure dintégration des données proposée permet d'obtenir des estimations de la conductivité en adéquation avec la structure à grande échelle ainsi que des predictions fiables des caractéristiques de transports sur des distances de moyenne à grande échelle. Les résultats correspondant au scénario de terrain indiquent que l'approche d'intégration des données nouvellement mise au point est capable d'appréhender correctement les hétérogénéitées à petite échelle aussi bien que les tendances à gande échelle du champ hydraulique prévalent. Les résultats montrent également une flexibilté remarquable et une robustesse de cette nouvelle approche dintégration des données. De ce fait, elle est susceptible d'être appliquée à un large éventail de données géophysiques et hydrologiques, à toutes les gammes déchelles. Dans la deuxième partie de ma thèse, j'évalue en détail la viabilité du réechantillonnage geostatique séquentiel comme mécanisme de proposition pour les méthodes Markov Chain Monte Carlo (MCMC) appliquées à des probmes inverses géophysiques et hydrologiques de grande dimension . L'objectif est de permettre une quantification plus précise et plus réaliste des incertitudes associées aux modèles obtenus. En considérant une série dexemples de tomographic radar puits à puits, j'étudie deux classes de stratégies de rééchantillonnage spatial en considérant leur habilité à générer efficacement et précisément des réalisations de la distribution postérieure bayésienne. Les résultats obtenus montrent que, malgré sa popularité, le réechantillonnage séquentiel est plutôt inefficace à générer des échantillons postérieurs indépendants pour des études de cas synthétiques réalistes, notamment pour le cas assez communs et importants où il existe de fortes corrélations spatiales entre le modèle et les paramètres. Pour résoudre ce problème, j'ai développé un nouvelle approche de perturbation basée sur une déformation progressive. Cette approche est flexible en ce qui concerne le nombre de paramètres du modèle et lintensité de la perturbation. Par rapport au rééchantillonage séquentiel, cette nouvelle approche s'avère être très efficace pour diminuer le nombre requis d'itérations pour générer des échantillons indépendants à partir de la distribution postérieure bayésienne. - Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending corresponding approaches beyond the local scale still represents a major challenge, yet is critically important for the development of reliable groundwater flow and contaminant transport models. To address this issue, I have developed a hydrogeophysical data integration technique based on a two-step Bayesian sequential simulation procedure that is specifically targeted towards larger-scale problems. The objective is to simulate the distribution of a target hydraulic parameter based on spatially exhaustive, but poorly resolved, measurements of a pertinent geophysical parameter and locally highly resolved, but spatially sparse, measurements of the considered geophysical and hydraulic parameters. To this end, my algorithm links the low- and high-resolution geophysical data via a downscaling procedure before relating the downscaled regional-scale geophysical data to the high-resolution hydraulic parameter field. I first illustrate the application of this novel data integration approach to a realistic synthetic database consisting of collocated high-resolution borehole measurements of the hydraulic and electrical conductivities and spatially exhaustive, low-resolution electrical conductivity estimates obtained from electrical resistivity tomography (ERT). The overall viability of this method is tested and verified by performing and comparing flow and transport simulations through the original and simulated hydraulic conductivity fields. The corresponding results indicate that the proposed data integration procedure does indeed allow for obtaining faithful estimates of the larger-scale hydraulic conductivity structure and reliable predictions of the transport characteristics over medium- to regional-scale distances. The approach is then applied to a corresponding field scenario consisting of collocated high- resolution measurements of the electrical conductivity, as measured using a cone penetrometer testing (CPT) system, and the hydraulic conductivity, as estimated from electromagnetic flowmeter and slug test measurements, in combination with spatially exhaustive low-resolution electrical conductivity estimates obtained from surface-based electrical resistivity tomography (ERT). The corresponding results indicate that the newly developed data integration approach is indeed capable of adequately capturing both the small-scale heterogeneity as well as the larger-scale trend of the prevailing hydraulic conductivity field. The results also indicate that this novel data integration approach is remarkably flexible and robust and hence can be expected to be applicable to a wide range of geophysical and hydrological data at all scale ranges. In the second part of my thesis, I evaluate in detail the viability of sequential geostatistical resampling as a proposal mechanism for Markov Chain Monte Carlo (MCMC) methods applied to high-dimensional geophysical and hydrological inverse problems in order to allow for a more accurate and realistic quantification of the uncertainty associated with the thus inferred models. Focusing on a series of pertinent crosshole georadar tomographic examples, I investigated two classes of geostatistical resampling strategies with regard to their ability to efficiently and accurately generate independent realizations from the Bayesian posterior distribution. The corresponding results indicate that, despite its popularity, sequential resampling is rather inefficient at drawing independent posterior samples for realistic synthetic case studies, notably for the practically common and important scenario of pronounced spatial correlation between model parameters. To address this issue, I have developed a new gradual-deformation-based perturbation approach, which is flexible with regard to the number of model parameters as well as the perturbation strength. Compared to sequential resampling, this newly proposed approach was proven to be highly effective in decreasing the number of iterations required for drawing independent samples from the Bayesian posterior distribution.

Multiple-scale hydrological and geophysical data integration through non-linear Bayesian sequential simulation

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the scale of a field site represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed downscaling procedure based on a non-linear Bayesian sequential simulation approach. The main objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity logged at collocated wells and surface resistivity measurements, which are available throughout the studied site. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariatekernel density function. Then a stochastic integration of low-resolution, large-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities is applied. The overall viability of this downscaling approach is tested and validated by comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure allows obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

Regional-scale integration of multi-scale hydrological and geophysical data using a two-step Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale for the purpose of improving predictions of groundwater flow and solute transport. However, extending corresponding approaches to the regional scale still represents one of the major challenges in the domain of hydrogeophysics. To address this problem, we have developed a regional-scale data integration methodology based on a two-step Bayesian sequential simulation approach. Our objective is to generate high-resolution stochastic realizations of the regional-scale hydraulic conductivity field in the common case where there exist spatially exhaustive but poorly resolved measurements of a related geophysical parameter, as well as highly resolved but spatially sparse collocated measurements of this geophysical parameter and the hydraulic conductivity. To integrate this multi-scale, multi-parameter database, we first link the low- and high-resolution geophysical data via a stochastic downscaling procedure. This is followed by relating the downscaled geophysical data to the high-resolution hydraulic conductivity distribution. After outlining the general methodology of the approach, we demonstrate its application to a realistic synthetic example where we consider as data high-resolution measurements of the hydraulic and electrical conductivities at a small number of borehole locations, as well as spatially exhaustive, low-resolution estimates of the electrical conductivity obtained from surface-based electrical resistivity tomography. The different stochastic realizations of the hydraulic conductivity field obtained using our procedure are validated by comparing their solute transport behaviour with that of the underlying ?true? hydraulic conductivity field. We find that, even in the presence of strong subsurface heterogeneity, our proposed procedure allows for the generation of faithful representations of the regional-scale hydraulic conductivity structure and reliable predictions of solute transport over long, regional-scale distances.

The TRANSMED Atlas: geological-geophysical fabric of the Mediterranean region - Final report of the project

Geological research on the Mediterranean region is presently characterized by the transition from disciplinary to multidisciplinary research, as well as from national to international investigations. In order to synthesize and integrate the vast disciplinary and national datasets which are available, it is necessary to implement maximum interaction among geoscientists of different backgrounds. The creation of project-oriented task forces in universities and other research institutions, as well as the development of large international cooperation programs, is instrumental in pursuing such a multidisciplinary and supranational approach. The TRANSMED Atlas, an official publication of the 32nd International Geological Congress (Florence 2004), is the result of an international scientific cooperation program which brought together for over two years sixty-three structural geologists, geophysicists, marine geologists, petrologists, sedimentologists, stratigraphers, paleogeographers, and petroleum geologists coming from eighteen countries, and working for the petroleum industry, academia, and other institutions, both public and private. The TRANSMED Atlas provides an updated, synthetic, and coherent portrayal of the overall geological-geophysical structure of the Mediterranean domain and the surrounding areas. The initial stimulus for the Atlas came from the realization of the extremely heterogeneous nature of the existing geological-geophysical data about such domain. These data have been gathered by universities, oil companies, geological surveys and other institutions in several countries, often using different procedures and standards. In addition, much of these data are written in languages and published in outlets that are not readily accessible to the general international reader. By synthesizing and integrating a wealth of preexisting and new data derived from surficial geology, seismic sections at various scales, and mantle tomographies, the TRANSMED Atlas provides for the first time a coherent geological overview of the Mediterranean region and represents an ideal springboard for future studies.

Bayesian Markov chain Monte Carlo inversion of geophysical data for the purpose of estimating the hydraulic properties of the unsaturated zone

Ground-penetrating radar (GPR) has the potential to provide valuable information on hydrological properties of the vadose zone because of their strong sensitivity to soil water content. In particular, recent evidence has suggested that the stochastic inversion of crosshole GPR data within a coupled geophysical-hydrological framework may allow for effective estimation of subsurface van-Genuchten-Mualem (VGM) parameters and their corresponding uncertainties. An important and still unresolved issue, however, is how to best integrate GPR data into a stochastic inversion in order to estimate the VGM parameters and their uncertainties, thus improving hydrological predictions. Recognizing the importance of this issue, the aim of the research presented in this thesis was to first introduce a fully Bayesian inversion called Markov-chain-Monte-carlo (MCMC) strategy to perform the stochastic inversion of steady-state GPR data to estimate the VGM parameters and their uncertainties. Within this study, the choice of the prior parameter probability distributions from which potential model configurations are drawn and tested against observed data was also investigated. Analysis of both synthetic and field data collected at the Eggborough (UK) site indicates that the geophysical data alone contain valuable information regarding the VGM parameters. However, significantly better results are obtained when these data are combined with a realistic, informative prior. A subsequent study explore in detail the dynamic infiltration case, specifically to what extent time-lapse ZOP GPR data, collected during a forced infiltration experiment at the Arrenaes field site (Denmark), can help to quantify VGM parameters and their uncertainties using the MCMC inversion strategy. The findings indicate that the stochastic inversion of time-lapse GPR data does indeed allow for a substantial refinement in the inferred posterior VGM parameter distributions. In turn, this significantly improves knowledge of the hydraulic properties, which are required to predict hydraulic behaviour. Finally, another aspect that needed to be addressed involved the comparison of time-lapse GPR data collected under different infiltration conditions (i.e., natural loading and forced infiltration conditions) to estimate the VGM parameters using the MCMC inversion strategy. The results show that for the synthetic example, considering data collected during a forced infiltration test helps to better refine soil hydraulic properties compared to data collected under natural infiltration conditions. When investigating data collected at the Arrenaes field site, further complications arised due to model error and showed the importance of also including a rigorous analysis of the propagation of model error with time and depth when considering time-lapse data. Although the efforts in this thesis were focused on GPR data, the corresponding findings are likely to have general applicability to other types of geophysical data and field environments. Moreover, the obtained results allow to have confidence for future developments in integration of geophysical data with stochastic inversions to improve the characterization of the unsaturated zone but also reveal important issues linked with stochastic inversions, namely model errors, that should definitely be addressed in future research.

Constraints on the permeability structure of alluvial aquifers from P-wave sonic logs

Modern sonic logging tools designed for shallow environmental and engineering applications allow for P-wave phase velocity measurements over a wide frequency band. Methodological considerations indicate that, for saturated unconsolidated sediments in the silt to sand range and source frequencies ranging from approximately 1 to 30 kHz, the observable poro-elastic P-wave velocity dispersion is sufficiently pronounced to allow for reliable first-order estimations of the underlying permeability structure. These predictions have been tested on and verified for a surficial alluvial aquifer. Our results indicate that, even without any further calibration, the thus obtained permeability estimates as well as their variabilities within the pertinent lithological units are remarkably close to those expected based on the corresponding granulometric characteristics.

Fracture network characterization using hydrological and geophysical data

Les décisions de gestion des eaux souterraines doivent souvent être justiffées par des modèles quantitatifs d'aquifères qui tiennent compte de l'hétérogénéité des propriétés hydrauliques. Les aquifères fracturés sont parmi les plus hétérogènes et très difficiles à étudier. Dans ceux-ci, les fractures connectées, d'ouverture millimètrique, peuvent agir comme conducteurs hydrauliques et donc créer des écoulements très localisés. Le manque général d'informations sur la distribution spatiale des fractures limite la possibilité de construire des modèles quantitatifs de flux et de transport. Les données qui conditionnent les modèles sont généralement spatialement limitées, bruitées et elles ne représentent que des mesures indirectes de propriétés physiques. Ces limitations aux données peuvent être en partie surmontées en combinant différents types de données, telles que les données hydrologiques et de radar à pénétration de sol plus commun ément appelé géoradar. L'utilisation du géoradar en forage est un outil prometteur pour identiffer les fractures individuelles jusqu'à quelques dizaines de mètres dans la formation. Dans cette thèse, je développe des approches pour combiner le géoradar avec les données hydrologiques affn d'améliorer la caractérisation des aquifères fracturés. Des investigations hydrologiques intensives ont déjà été réalisées à partir de trois forage adjacents dans un aquifère cristallin en Bretagne (France). Néanmoins, la dimension des fractures et la géométrie 3-D des fractures conductives restaient mal connue. Affn d'améliorer la caractérisation du réseau de fractures je propose dans un premier temps un traitement géoradar avancé qui permet l'imagerie des fractures individuellement. Les résultats montrent que les fractures perméables précédemment identiffées dans les forages peuvent être caractérisées géométriquement loin du forage et que les fractures qui ne croisent pas les forages peuvent aussi être identiffées. Les résultats d'une deuxième étude montrent que les données géoradar peuvent suivre le transport d'un traceur salin. Ainsi, les fractures qui font partie du réseau conductif et connecté qui dominent l'écoulement et le transport local sont identiffées. C'est la première fois que le transport d'un traceur salin a pu être imagé sur une dizaines de mètres dans des fractures individuelles. Une troisième étude conffrme ces résultats par des expériences répétées et des essais de traçage supplémentaires dans différentes parties du réseau local. En outre, la combinaison des données de surveillance hydrologique et géoradar fournit la preuve que les variations temporelles d'amplitude des signaux géoradar peuvent nous informer sur les changements relatifs de concentrations de traceurs dans la formation. Par conséquent, les données géoradar et hydrologiques sont complémentaires. Je propose ensuite une approche d'inversion stochastique pour générer des modèles 3-D de fractures discrètes qui sont conditionnés à toutes les données disponibles en respectant leurs incertitudes. La génération stochastique des modèles conditionnés par géoradar est capable de reproduire les connexions hydrauliques observées et leur contribution aux écoulements. L'ensemble des modèles conditionnés fournit des estimations quantitatives des dimensions et de l'organisation spatiale des fractures hydrauliquement importantes. Cette thèse montre clairement que l'imagerie géoradar est un outil utile pour caractériser les fractures. La combinaison de mesures géoradar avec des données hydrologiques permet de conditionner avec succès le réseau de fractures et de fournir des modèles quantitatifs. Les approches présentées peuvent être appliquées dans d'autres types de formations rocheuses fracturées où la roche est électriquement résistive.