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Néven, Alexis
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Néven, Alexis
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- PublicationAccès libreStochastic multiple data integration for the characterization of quaternary aquifers(2023)
; La gestion des ressources en eaux souterraines nécessite souvent le développement de modèles géologiques et hydrogéologiques. Cependant, la construction de modèles précis peut s’avérer une tâche difficile et longue, en particulier dans les vastes zones présentant des dépôts quaternaires complexes. Or, ces zones sont souvent celles qui sont le plus fréquemment soumises à l’exploitation des ressources et à la pollution. Pour résoudre ce problème, plusieurs études ont proposé des méthodologies innovantes pour intégrer différents types de données, notamment des données sur les puits, des données géophysiques et des données hydrogéologiques. L’objectif est de faciliter la construction de ces modèles dans des cadres cohérents et reproductibles avec une estimation robuste des erreurs. Nous présentons ici quatre études qui proposent de nouvelles méthodologies pour relever ce défi. La première étude présente un vaste et dense ensemble de données électromagnétiques dans le domaine temporel (TDEM) acquises dans la haute vallée de l’Aar, en Suisse, afin d’améliorer la connaissance des variations spatiales des dépôts quaternaires. Les modèles de résistivité inversée dérivés de cette acquisition ont été publiés et pourraient être utilisés pour diverses études futures. Cette étude met également en évidence le potentiel de l’ensemble de données pour le développement d’algorithmes d’intégration de données en raison de l’abondance de diverses données librement disponibles sur la même zone. La deuxième étude propose une nouvelle méthodologie pour combiner les forages et les données géophysiques avec une propagation de l’incertitude pour prédire la probabilité d’argile à l’échelle d’une vallée. Une fonction de translation variant dans l’espace a été utilisée pour estimer la fraction d’argile à partir de la résistivité. Les paramètres de cette fonction sont inversés en utilisant la description des forages comme points de contrôle. Ils combinent cette estimation de la fraction d’argile avec un cadre d’interpolation stochastique 3D non déterministe basé sur un algorithme de statistiques à points multiples et une fonction aléatoire gaussienne afin d’obtenir un modèle 3D réaliste à haute résolution spatiale de la fraction d’argile pour la haute vallée de l’Aar. L’étude démontre la qualité des valeurs prédites et leurs incertitudes correspondantes en utilisant la validation croisée. La troisième étude porte sur la possibilité d’intégrer des données de forage, géophysiques et hydrogéologiques, tout en conservant la cohérence du concept géologique des modèles. Nous avons utilisé un générateur stochastique de modèles géologiques pour construire un ensemble de modèles préalables basés sur les forages. Nous proposons ensuite une approche d’inversion multi-échelle qui combine des modèles peu fidèles et moins précis avec des modèles plus fidèles et plus précis afin de réduire le temps nécessaire à la convergence de l’inversion. Les données géophysiques et hydrogéologiques sont intégrées à l’aide d’un algorithme ES-MDA (Ensemble Smoother with Multiple Data Assimilation Algorithm). Le flux de travail garantit que les modèles sont géologiquement cohérents et estime de manière robuste l’incertitude associée au modèle final. L’étude démontre l’efficacité de cette approche pour un cas synthétique contrôlé. Elle montre que ArchPY et ES-MDA sont capables de générer des réalisations plausibles de la subsurface pour les modèles sédimentologiques du Quaternaire. Enfin, la quatrième étude présente une méthodologie innovante qui combine l’algorithme ES-MDA avec un code de modélisation géologique hiérarchique open-source pour intégrer des sources de données multiples et construire des modèles géologiquement cohérents avec une estimation d’erreur robuste. La méthodologie est appliquée à un cas de terrain dans la haute vallée de l’Aar, en Suisse. Un cadre de validation croisée est mis en oeuvre afin d’évaluer la méthodologie. L’approche aboutit à des modèles finaux qui équilibrent efficacement la précision et l’incertitude et qui peuvent prendre en compte diverses sources de données, y compris des données géophysiques, des connaissances conceptuelles régionales, des forages et des mesures hydrogéologiques à l’échelle d’une vallée. En résumé, cette thèse présente plusieurs méthodes innovantes qui pourraient être appliquées à la réalisation de modèles hydrogéologiques à petite ou grande échelle. ABSTRACT Groundwater resource management often requires the development of geological and hydrogeological models. However, constructing accurate models can be a challenging and time-consuming task, especially in large areas with complex Quaternary deposits. However, these areas are often the most frequently subject to resource exploitation and pollution. To address this issue, several studies have proposed innovative methodologies to integrate various types of data, including wells, geophysical, and hydrogeological data. The objective is to facilitate the construction of these models within coherent and reproducible frameworks with robust error estimation. In these, we present four studies that present novel methodologies to address this challenge. The first study presents a large and dense Time Domain ElectroMagnetic (TDEM) dataset acquired in the upper Aare Valley, Switzerland, to improve knowledge of the spatial variations of Quaternary deposits. The inverted resistivity models derived from this acquisition were published and could be used for various future studies. It also highlights the data set’s potential for data integration algorithm development because of the abundance of various freely available data on the same zone. The second study proposes a new methodology to combine boreholes and geophysical data with a propagation of the uncertainty to predict the probability of clay at the scale of a valley. A spatially varying translator function was used to estimate the clay fraction from resistivity. The parameters of this function are inverted using the description of the boreholes as control points. They combine this clay fraction estimation with a nondeterministic 3D stochastic interpolation framework based on a Multiple Points Statistics algorithm and Gaussian Random Function to obtain a 3D realistic high spatial resolution model of clay fraction for the upper Aare valley. The study demonstrates the quality of the predicted values and their corresponding uncertainties using cross-validation. The third study addresses the possibility of integrating boreholes, geophysical, and hydrogeological data, while keeping the geological concept of the models coherent. We used a stochastic geological model generator to construct a set of prior models based on the boreholes. We then propose a multiscale inversion approach that combines low-fidelity and less accurate models with high-fidelity and more accurate models to reduce the time needed for the inversion to converge. Both geophysical and hydrogeological data are integrated, using an Ensemble Smoother with Multiple Data Assimilation Algorithm (ES-MDA) algorithm. The workflow ensures that the models are geologically consistent and robustly estimate the associated uncertainty with the final model. The study demonstrates the effectiveness of this approach for a controlled synthetic case. It shows that ArchPY and ES-MDA are capable of generating plausible subsurface realizations for Quaternary Sedimentological Models. Finally, the fourth study presents an innovative methodology that combines the ES-MDA algorithm with an open-source hierarchical geological modeling code to integrate multiple data sources and construct geologically consistent models with robust error estimation. The methodology is applied to a field case in the upper Aare Valley, Switzerland. In order to benchmark the methodology, a cross-validation framework is implemented. The approach results in final models that effectively balance accuracy and uncertainty and can take into account various data sources, including geophysical data, regional conceptual knowledge, boreholes, and hydrogeological measurements at a valley scale. In summary, this thesis presents several innovative methods that could be applied on small to large scale hydrogeological model realization. - PublicationAccès libreA Novel Methodology for the Stochastic Integration of Geophysical and Hydrogeological Data in Geologically Consistent Models(2023)
; AbstractTo address groundwater issues, it is often necessary to develop geological and hydrogeological models. Combining geological, geophysical and hydrogeological data available on a site to build such models is often a challenge. This paper presents a methodology to integrate such data within a geologically consistent model with robust error estimation. The methodology combines the Ensemble Smoother with Multiple Data Assimilation (ESMDA) algorithm with a hierarchical geological modeling approach (ArchPy). Geophysical and hydrogeological field data are jointly assimilated in a stochastic ESMDA framework. To speed up the inversion process, forward responses are computed in lower‐dimensional spaces relevant to each physical problem. By doing so, the final models take into account multiple data sources and regional conceptual geological knowledge. This study illustrates the applicability of this novel approach using actual data from the upper Aare Valley, Switzerland. The results of cross‐validation show that the combination of different data types, each sensitive to different spatial dimensions, enhances the quality of the model within a reasonable computing time. The proposed methodology allows the automatic generation of groundwater models with robust uncertainty estimation and could be applied to a wide variety of hydrogeological issues. - PublicationAccès libreProbabilistic estimation of tunnel inflow from a karstic conduit network(2023)
; ; - PublicationAccès libreAutomatic stochastic 3D clay fraction model from tTEM survey and borehole data(2022)
; - PublicationAccès libreStochastic multi-fidelity joint hydrogeophysical inversion of consistent geological models(2022)
; ; In Quaternary deposits, the characterization of subsurface heterogeneity and its associated uncertainty is critical when dealing with groundwater resource management. The combination of different data types through joint inversion has proven to be an effective way to reduce final model uncertainty. Moreover, it allows the final model to be in agreement with a wider spectrum of data available on site. However, integrating them stochastically through an inversion is very time-consuming and resource expensive, due to the important number of physical simulations needed. The use of multi-fidelity models, by combining low-fidelity inexpensive and less accurate models with high-fidelity expensive and accurate models, allows one to reduce the time needed for inversion to converge. This multiscale logic can be applied for the generation of Quaternary models. Most Quaternary sedimentological models can be considered as geological units (large scale), populated with facies (medium scale), and finally completed by physical parameters (small scale). In this paper, both approaches are combined. A simple and fast time-domain EM 1D geophysical direct problem is used to first constrain a simplified stochastic geologically consistent model, where each stratigraphic unit is considered homogeneous in terms of facies and parameters. The ensemble smoother with multiple data assimilation (ES-MDA) algorithm allows generating an ensemble of plausible subsurface realizations. Fast identification of the large-scale structures is the main point of this step. Once plausible unit models are generated, high-fidelity transient groundwater flow models are incorporated. The low-fidelity models are populated stochastically with heterogeneous facies and their associated parameter distribution. ES-MDA is also used for this task by directly inferring the property values (hydraulic conductivity and resistivity) from the generated model. To preserve consistency, geophysical and hydrogeological data are inverted jointly. This workflow ensures that the models are geologically consistent and are therefore less subject to artifacts due to localized poor-quality data. It is able to robustly estimate the associated uncertainty with the final model. Finally, due to the simplification of both the direct problem and the geology during the low-fidelity part of the inversion, it greatly reduces the time required to converge to an ensemble of complex models while preserving consistency. - PublicationAccès libreProbabilistic prediction of karst water inflow during construction of underground structures(2022)
; ; AbstractVarious methods have been developed in recent decades to predict hazards associated with karst voids in underground construction. Common to all these methods is that the predicted range of water inflow is often insufficient for the purpose of implementing the planned construction works. This is usually due to an incomplete knowledge of the karst conduit system within a project area, making it difficult to predict the position and characteristics of karst voids. The method presented in this paper permits a robust prediction of karst water inflow. It is based on a combination of stochastically generated, pseudo‐genetic karst conduit systems and hydraulic modelling of the hydrogeological conditions using a Monte Carlo approach. This approach facilitates a plausible estimation of the expected range of karst‐induced water inflows and also enables the probability of encountering a karst voids. to be determined. The predictions allow for differentiated treatment of the hazards associated with karst water during the construction and operation phase of underground structures. In concrete terms, this relates to the planning and implementation of exploratory measures and ground‐improvement measures, the design of the dewatering system and its monitoring during the construction and operation phase. - PublicationAccès libreIce volume and basal topography estimation using geostatistical methods and GPR measurements: Application on the Tsanfleuron and Scex Rouge glacier, Swiss Alps(2021-7)
; ; ; ; Ground Penetrating Radar (GPR) is nowadays widely used for determining glacier thickness. However, this method provides thickness data only along the acquisition lines and therefore interpolation has to be made between them. Depending on the interpolation strategy, calculated ice volumes can differ and can lack an accurate error estimation. Furthermore, glacial basal topography is often characterized by complex geomorphological features, which can be hard to reproduce using classical 5 interpolation methods, especially when the conditioning data are sparse or when the morphological features are too complex. This study investigates the applicability of multiple-point statistics (MPS) simulations to interpolate glacier bedrock topography using GPR measurements. In 2018, a dense GPR data set was acquired on the Tsanfleuron Glacier (Switzerland). The results obtained with the direct sampling MPS method are compared against those obtained with kriging and sequential Gaussian simulations (SGS) on both a synthetic data set – with known reference volume and bedrock topography – and the real data 10 underlying the Tsanfleuron glacier. Using the MPS modelled bedrock, the ice volume for the Scex Rouge and Tsanfleuron Glacier is estimated to be 113.9 ± 1.6 Miom3 . The direct sampling approach, unlike the SGS and the kriging, allowed not only an accurate volume estimation but also the generation of a set of realistic bedrock simulations. The complex karstic geomorphological features are reproduced, and can be used to significantly improve for example the precision of under-glacial flow estimation. - PublicationAccès libretTEM20AAR: a benchmark geophysical data set for unconsolidated fluvioglacial sediments(2021-6)
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