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Renard, Philippe
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Renard, Philippe
Affiliation principale
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Directeur de Recherche
Email
Philippe.Renard@unine.ch
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Voici les éléments 1 - 10 sur 190
- PublicationAccès libreExploring substitution random functions composed of stationary multi-Gaussian processes(2024)
; Simulation of random felds is widely used in Earth sciences for modeling and uncertainty quantifcation. The spatial features of these felds may have a strong impact on the forecasts made using these felds. For instance, in fow and transport problems the connectivity of the permeability felds is a crucial aspect. Multi-Gaussian random felds are the most common tools to analyze and model continuous felds. Their spatial correlation structure is described by a covariance or variogram model. However, these types of spatial models are unable to represent highly or poorly connected structures even if a broad range of covariance models can be employed. With this type of model, the regions with values close to the mean are always well connected whereas the regions of low or high values are isolated. Substitution random functions (SRFs) belong to another broad class of random functions that are more fexible. SRFs are constructed by composing (Z = Y◦T) two stochastic processes: the directing function T (latent feld) and the coding process Y (modifying the latent feld in a stochastic manner). In this paper, we study the properties of SRFs obtained by combining stationary multi-Gaussian random felds for both T and Y with bounded variograms. The resulting SRFs Z are stationary, but as T has a fnite variance Z is not ergodic for the mean and the covariance. This means that single realizations behave diferently from each other. We propose a simple technique to control which values (low, intermediate, or high) are connected. It consists of adding a control point on the process Y to guide every single realization. The conditioning to local values is obtained using a Gibbs sampler. - PublicationAccès libreFracture density reconstruction using direct sampling multiple-point statistics and extreme value theory(2024)
; - PublicationAccès libreUsing multiple-point geostatistics for geomodeling of a vein-type gold deposit(2024)
; - PublicationAccès libreComparison of three recent discrete stochastic inversion methods and influence of the prior choice(2024)
; ; Groundwater flow depends on subsurface heterogeneity, which often calls for categorical fields to represent different geological facies. The knowledge about subsurface is however limited and often provided indirectly by state variables, such as hydraulic heads of contaminant concentrations. In such cases, solving a categorical inverse problem is an important step in subsurface modeling. In this work, we present and compare three recent inverse frameworks: Posterior Population Expansion (PoPEx), Ensemble Smoother with Multiple Data Assimilation (ESMDA), and DREAM-ZS (a Markov chain Monte Carlo sampler). PoPEx and ESDMA are used with Multiple-point statistics (MPS) as geostatistical engines, and DREAM-ZS is used with a Wasserstein generative adversarial network (WGAN). The three inversion methods are tested on a synthetic example of a pumping test in a fluvial channelized aquifer. Moreover, the inverse problem is solved three times with each method, each time using a different training image to check the performance of the methods with different geological priors. To assess the quality of the results, we propose a framework based on continuous ranked probability score (CRPS), which compares single true values with predictive distributions. All methods performed well when using the training image used to create the reference, but their performances were degraded with the alternative training images. PoPEx produced the least geological artifacts but presented a rather slow convergence. ESMDA showed initially a very fast convergence which reaches a plateau, contrary to the remaining methods. DREAM-ZS was overly confident in placing some incorrect geological features but outperformed the other methods in terms of convergence. - PublicationAccès libreData integration and automated geological modeling of Quaternary aquifersCette thèse a pour sujet d’étude la modélisation de l’hétérogénéité des aquifères et formations géologiques du Quaternaire. Plus particulièrement, elle cherche à intégrer au mieux les données géologiques existantes au sein des procédures de modélisation et à trouver des façons de les automatiser au maximum. Différents axes de recherches sont investigués afin de répondre à ces problématiques. Dans un premier temps, une revue littéraire des méthodes de modélisation de faciès, caractérisant largement l’hétérogénéité du sous-sol, est réalisée afin d’avoir une vue aussi complète que possible sur les algorithmes et méthodes existantes. Le but était aussi de proposer une classification récente et actualisée de ces méthodes dans le but d’aider à la sélection d’une méthode en fonction des situations. Les potentielles lacunes en matière de recherche dans ce domaine sont également identifiées. A la suite de cela, une proposition d’une approche hiérarchique, semi-automatique et stochastique, appelée ArchPy est proposée. Elle permet de facilement combiner l’expertise géologique, représentée sous la forme d’une pile stratigraphique, et les différentes données géologiques. Cette approche hiérarchique opère en trois étapes: unité stratigraphique, faciès et propriétés pétrophysiques. Elles représentent différentes échelles de complexité géologique et permettent d’atteindre une grande complexité en terme d’hétérogénéité. ArchPy a ensuite été utilisé sur différents sites géologiques afin de démontrer ses capacités de modélisation. Cependant, ArchPy est confronté à certains problèmes qui limitent son automatisation. En particulier, la difficulté associée à la construction de la pile stratigraphique a conduit à la proposition d’un nouvel algorithme pour déterminer automatiquement la pile stratigraphique sur la base des données lithologiques des forages. Une méthode est également proposée afin de résoudre le problème récurrent des informations stratigraphiques manquantes dans les données de forages. Une fois l’approche ArchPy mise en place, il est nécessaire d’affiner sa deuxième étape - la modélisation des faciès - qui est particulièrement délicate à modéliser en fonction du contexte géologique. Dans ce but, une nouvelle méthode de modélisation des faciès, appelée EROSim, permet de mieux représenter les structures sédimentaires. Cette méthode a la particularité qu’elle utilise des surfaces pour représenter les différents évènements géologiques pouvant affecter l’hétérogénéité d’une formation sédimentaire. Les outils et méthodes présentées montrent l’intérêt et le bénéfice des méthodes stochastiques, automatisées et en accès libre pour la modélisation géologique. ArchPy pose notamment les bases d’une boite à outils de modélisation géologique stochastique ayant un large panel d’utilisations, tel que la réalisation de modèles géologiques complexes ou le couplage dans des processus d’inversion. Cette thèse participe ainsi au développement d’une modélisation géologique devenant de plus en plus accessible et libre d’accès, pour le bien commun de la communauté géoscientifique, et au-delà. ABSTRACT The subject of this thesis is the modeling of heterogeneity in Quaternary aquifers and geological formations. More specifically, it seeks to integrate existing geological data into modeling procedures and find ways of automating them as far as possible. Various lines of research are being investigated to address these challenges. First, a literature review of facies modeling methods, characterizing the heterogeneity of the subsurface, is carried out in order to gain an overview as complete as possible of existing algorithms and methods. The aim was also to propose a recent and up-to-date classification of these methods, to help in the selection of a method according to the situation. Potential research gaps in this field are also identified. Second, an approach and module is developed to tackle the challenges of the thesis. The result is the proposal of a hierarchical, semi-automatic and stochastic approach, called ArchPy, which makesit easy to combine geological expertise, represented in the form of a stratigraphic pile, and various geological data. This hierarchical approach operatesin three stages: stratigraphic unit, facies and petrophysical properties. These represent different scales of geological complexity, and enable a high degree of heterogeneity to be achieved. In order to demonstrate ArchPy’s usefulness, it is then applied to different geological sites and improved at several pointsdepending on the situation. However, ArchPy faces some issues that limits its automation. In particular, the difficulty associated with the construction of the stratigraphic pile led to the proposition of a new algorithm to automatically determine the stratigraphic pile based on borehole records. A method is also proposed to solve the recurrent problem of missing stratigraphic information in drilling data. Once ArchPy approach is set up, it is necessary to refine its second step - facies modeling - which is particularly to accurately model depending on the geological context. To this aim, a novel facies modeling methods, called EROSim, to better represent sedimentary structures is proposed. This method is distinctive because it employs surfaces to represent various geological events that can impact the heterogeneity of a sedimentary formation. The presented tools and methods showcase the advantages and usefulness of stochastic, automated, and open-access methods for geological modeling. Specifically, ArchPy establishes the groundwork for a stochastic geological modeling toolbox with a broad range of applications, including the creation of intricate geological models or coupling in inversion processes. This thesis contributes to the development of geological modeling, which is becoming more accessible and available to the geoscientific community and beyond.
- PublicationMétadonnées seulementMultiresolution Approach to Condition Categorical Multiple-Point Realizations to Dynamic Data With Iterative Ensemble Smoothing(2023-1-19)
; ; A new methodology is presented for the conditioning of categorical multiple-point statistics (MPS) simulations to dynamic data with an iterative ensemble smoother (ES-MDA). The methodology relies on a novel multiresolution parameterization of the categorical MPS simulation. The ensemble of latent parameters is initially defined on the basis of the coarsest-resolution simulations of an ensemble of multiresolution MPS simulations. Because this ensemble is non-multi-Gaussian, additional steps prior to the computation of the first update are proposed. In particular, the parameters are updated at predefined locations at the coarsest scale and integrated as hard data to generate a new multiresolution MPS simulation. The performance of the methodology was assessed on a synthetic groundwater flow problem inspired from a real situation. The results illustrate that the method converges towards a set of final categorical realizations that are consistent with the initial categorical ensemble. The convergence is reliable in the sense that it is fully controlled by the integration of the ES-MDA update into the new conditional multiresolution MPS simulations. Thanks to a massively reduced number of parameters compared to the size of the categorical simulation, the identification of the geological structures during the data assimilation is particularly efficient for this example. The comparison between the estimated uncertainty and a reference estimate obtained with a Monte Carlo method shows that the uncertainty is not severely reduced during the assimilation as is often the case. The connectivity is successfully reproduced during the iterative procedure despite the rather large distance between the observation points. - PublicationAccès libreA new perspective to model subsurface stratigraphy in alluvial hydrogeological basins, introducing geological hierarchy and relative chronology(2023-1-17)
- PublicationAccès libreTowards Improved Remedial Outcomes in Categorical Aquifers with an Iterative Ensemble Smoother(2023)
; ; - 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 libreImproving understanding of groundwater flow in an alpine karst system by reconstructing its geologic history using conduit network model ensembles(2023)
; ; Nico GoldscheiderAbstract. Reconstructing the geologic history of a karst area can advance understanding of the system's present-day hydrogeologic functioning and help predict the location of unexplored conduits. This study tests competing hypotheses describing past conditions controlling cave formation in an alpine karst catchment, by comparing an ensemble of modeled networks to the observed network map. The catchment, the Gottesacker karst system (Germany and Austria), is drained by three major springs and a paleo-spring and includes the partially explored Hölloch cave, which consists of an active section whose formation is well-understood and an inactive section whose formation is the subject of debate. Two hypotheses for the formation of the inactive section are the following: (1) glaciation obscured the three present-day springs, leaving only the paleo-spring, or (2) the lowest of the three major springs (Sägebach) is comparatively young, so its subcatchment previously drained to the paleo-spring. These hypotheses were tested using the pyKasso Python library (built on anisotropic fast-marching methods) to generate two ensembles of networks, one representing each scenario. Each ensemble was then compared to the known cave map. The simulated networks generated under hypothesis 2 match the observed cave map more closely than those generated under hypothesis 1. This supports the conclusion that the Sägebach spring is young, and it suggests that the cave likely continues southwards. Finally, this study extends the applicability of model ensemble methods from situations where the geologic setting is known but the network is unknown to situations where the network is known but the geologic evolution is not.