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Renard, Philippe
Nom
Renard, Philippe
Affiliation principale
Fonction
Directeur de Recherche
Email
Philippe.Renard@unine.ch
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6 Résultats
Voici les éléments 1 - 6 sur 6
- PublicationAccès libreAnalysis and stochastic simulation of geometrical properties of conduits in karstic networks(2020-11)
; Viseur, SophieDespite intensive explorations by speleologists, karstic systems remain only partially described as many conduits are not accessible to humans. The classical exploration techniques produce sparse data, leading to various uncertainties about the conduit dimensions, essential parameters for flow simulations. Stochastic simulations offer a possibility to better assess these uncertainties. In this paper, we propose different methods to stochastically simulate the properties (size and shape anisotropy) of karstic conduits on already existing skeletons. These approaches, based on Sequential Gaussian Simulations (SGS), allow taking different conditioning data into account, while respecting the intricacy of the networks. To infer the input parameters, we perform a statistical study of the conduit dimensions of 49 explored karstic networks, focusing on their equivalent radius and width-height ratio. Thanks to the definition of 1D curvilinear variograms, we demonstrate the existence of a spatial correlation along the networks, which is even higher when considering independently each conduit. Finally, using ad hoc algorithms implemented for computing both a conduit hierarchy inside karstic networks and a relative position regarding outputs, we find no evidence of an obvious link between these two entities and the studied metrics. The simulation methods are then demonstrated on the karstic network of Arrestelia (Pyrénées-Atlantiques, France), and show the consistency of the proposed approach with the observations made on the explored natural systems. - PublicationAccès libreA model ensemble generator to explore structural uncertainty in karst systems with unmapped conduits(2020-10)
; Goldscheider, NicoKarst aquifers are characterized by high-conductivity conduits embedded in a low-conductivity fractured matrix, resulting in extreme heterogeneity and variable groundwater flow behavior. The conduit network controls groundwater flow, but is often unmapped, making it difficult to apply numerical models to predict system behavior. This paper presents a multi-model ensemble method to represent structural and conceptual uncertainty inherent in simulation of systems with limited spatial information, and to guide data collection. The study tests the new method by applying it to a well-mapped, geologically complex long-term study site: the Gottesacker alpine karst system (Austria/Germany). The ensemble generation process, linking existing tools, consists of three steps: creating 3D geologic models using GemPy (a Python package), generating multiple conduit networks constrained by the geology using the Stochastic Karst Simulator (a MATLAB script), and, finally, running multiple flow simulations through each network using the Storm Water Management Model (C-based software) to reject nonbehavioral models based on the fit of the simulated spring discharge to the observed discharge. This approach captures a diversity of plausible system configurations and behaviors using minimal initial data. The ensemble can then be used to explore the importance of hydraulic flow parameters, and to guide additional data collection. For the ensemble generated in this study, the network structure was more determinant of flow behavior than the hydraulic parameters, but multiple different structures yielded similar fits to the observed flow behavior. This suggests that while modeling multiple network structures is important, additional types of data are needed to discriminate between networks. - PublicationAccès libreAn optical laser device for mapping 3D geometry of underwater karst structures: first tests in the Ox Bel’Ha system, Yucatan, Mexico(2016)
- PublicationAccès libreA method for the stochastic modeling of karstic systems accounting for geophysical data: an example of application in the region of Tulum, Yucatan Peninsula (Mexico)(2013-1-10)
; ; - PublicationAccès libreA pseudo-genetic stochastic model to generate karstic networks(2012)
; ; Jenni, SandraIn this paper, we present a methodology for the stochastic simulation of 3D karstic conduits accounting for conceptual knowledge about the speleogenesis processes and accounting for a wide variety of field measurements.
The methodology consists of four main steps. First, a 3D geological model of the region is built. The second step consists in the stochastic modeling of the internal heterogeneity of the karst formations (e.g. initial fracturation, bedding planes, inception horizons, etc.). Then a study of the regional hydrology/hydrogeology is conducted to identify the potential inlets and outlets of the system, the base levels and the possibility of having different phases of karstification. The last step consists in generating the conduits in an iterative manner using a fast marching algorithm. In most of these steps, a probabilistic model can be used to represent the degree of knowledge available and the remaining uncertainty depending on the data at hand.
The conduits are assumed to follow minimum effort paths in a heterogeneous medium from sinkholes or dolines toward springs. The search of the shortest path is performed using a fast marching algorithm. This process can be iterative, allowing to account for the presence of already simulated conduits and to produce a hierarchical network.
The final result is a stochastic ensemble of 3D karst reservoir models that are all constrained by the regional geology, the local heterogeneities and the regional flow conditions. These networks can then be used to simulate flow and transport. Several levels of uncertainty can be considered (large scale geological structures, local heterogeneity, position of possible inlets and outlets, phases of karstification).
Compared to other techniques, this method is fast, to account for the main factors controlling the 3D geometry of the network, and to allow conditioning from available field observations. - PublicationAccès libreA method for the stochastic modeling of karstic systems accounting for geophysical data:: an example of application in the region of Tulum, Yucatan Peninsula (Mexico)
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