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Dall Alba, Valentin

Nom
Dall Alba, Valentin
Affiliation principale
Fonction
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https://libra.unine.ch/handle/123456789/31920
_
0000-0002-4883-0869

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  • Publication
    Accès libre
    Probabilistic estimation of tunnel inflow from a karstic conduit network
    (2023)
    Dall Alba, Valentin 
    ;
    Alexis Neven 
    ;
    Rob de Rooij
    ;
    Marco Filipponi
    ;
    Renard, Philippe 
    When planning infrastructures such as tunnels in karstified formations, a risk assessment of groundwater inflow must be conducted. The aim of this paper is to present a workflow for the probabilistic estimation of the water inflow from karst conduits using a Monte-Carlo approach. The procedure involves three main steps. First, realistic stochastic karstic conduit network geometries are generated based on fracture and stratigraphic information using the Stochastic Karstic Simulation approach (SKS). To represent the geological uncertainty, different scenarios are considered. Then, a discrete–continuum numerical modeling approach is employed, allowing the flow calculation to account for the exchange between the matrix and the conduits as well as the transition between turbulent and laminar flow in the conduits. Because it is not known if and where (at which depths) the tunnel may hit a karst conduit, and what will be the pressure gradient in the system, different hydrogeological scenarios are considered in the uncertainty analysis phase including a randomized location of the tunnel, a range of possible pressure gradients, and a range of possible matrix permeability values. The final step consists of the statistical analysis of the results. The proposed workflow allows estimating the range of plausible inflows and studying how the inflows are related to the network geometry properties and to the hydrodynamic parameters of the aquifer. This method is illustrated in a simple synthetic but realistic case of a rather deep and confined karstic formation. In that situation, the results show that even if the pressure difference in the system and the matrix permeability value are important factors controlling the long-term inflow, the karstic conduit network geometry and connectivity also play a critical role in the determination of the potential discharge. Overall, this study demonstrates the possibility and advantages of using stochastic analysis in the early phases of project planning to predict possible long-term water inflow in tunnel after its construction.
  • Publication
    Accès libre
    Probabilistic prediction of karst water inflow during construction of underground structures
    (2022)
    Marco Filipponi
    ;
    Renard, Philippe 
    ;
    Dall Alba, Valentin 
    ;
    Néven, Alexis 
    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.
  • Publication
    Accès libre
    Ice volume and basal topography estimation using geostatistical methods and GPR measurements: Application on the Tsanfleuron and Scex Rouge glacier, Swiss Alps
    (2021-7)
    Néven, Alexis 
    ;
    Dall Alba, Valentin 
    ;
    Juda, Przemyslaw 
    ;
    Straubhaar, Julien 
    ;
    Renard, Philippe 
    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.
  • Publication
    Accès libre
    3D multiple-point statistics simulations of the Roussillon Continental Pliocene aquifer using DeeSse
    (2020-10)
    Dall Alba, Valentin 
    ;
    Renard, Philippe 
    ;
    Straubhaar, Julien 
    ;
    Issautier, Benoît
    ;
    Cabellero, Yvan
    This study introduces a novel workflow to model the heterogeneity of complex aquifers using the multiplepoint statistics algorithm DeeSse. We illustrate the approach by modeling the Continental Pliocene layer of the Roussillon aquifer in the region of Perpignan (southern France). When few direct observations are available, statistical inference from field data is difficult if not impossible and traditional geostatistical approaches cannot be applied directly. By contrast, multiple-point statistics simulations can rely on one or several alternative conceptual geological models provided using training images (TIs). But since the spatial arrangement of geological structures is often non-stationary and complex, there is a need for methods that allow to describe and account for the non-stationarity in a simple but efficient manner. The main aim of this paper is therefore to propose a workflow, based on the direct sampling algorithm DeeSse, for these situations. The conceptual model is provided by the geologist as a 2D non-stationary training image in map view displaying the possible organization of the geological structures and their spatial evolution. To control the non-stationarity, a 3D trend map is obtained by solving numerically the diffusivity equation as a proxy to describe the spatial evolution of the sedimentary patterns, from the sources of the sediments to the outlet of the system. A 3D continuous rotation map is estimated from inferred paleoorientations of the fluvial system. Both trend and orientation maps are derived from geological insights gathered from outcrops and general knowledge of processes occurring in these types of sedimentary environments. Finally, the 3D model is obtained by stacking 2D simulations following the paleotopography of the aquifer. The vertical facies transition between successive 2D simulations is controlled partly by the borehole data used for conditioning and by a sampling strategy. This strategy accounts for vertical probability of transitions, which are derived from the borehole observations, and works by simulating a set of conditional data points from one layer to the next. This process allows us to bypass the creation of a 3D training image, which may be cumbersome, while honoring the observed vertical continuity.