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Pirot, Guillaume

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Pirot, Guillaume
Affiliation principale
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https://libra.unine.ch/handle/123456789/168

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  • Publication
    Accès libre
    Contaminant source localization via Bayesian global optimization
    (2019)
    Pirot, Guillaume 
    ;
    Tipaluck Krityakierne
    ;
    David Ginsbourger
    ;
    Renard, Philippe 
    Abstract. Contaminant source localization problems require efficient and robust methods that can account for geological heterogeneities and accommodate relatively small data sets of noisy observations. As realism commands hi-fidelity simulations, computation costs call for global optimization algorithms under parsimonious evaluation budgets. Bayesian optimization approaches are well adapted to such settings as they allow the exploration of parameter spaces in a principled way so as to iteratively locate the point(s) of global optimum while maintaining an approximation of the objective function with an instrumental quantification of prediction uncertainty. Here, we adapt a Bayesian optimization approach to localize a contaminant source in a discretized spatial domain. We thus demonstrate the potential of such a method for hydrogeological applications and also provide test cases for the optimization community. The localization problem is illustrated for cases where the geology is assumed to be perfectly known. Two 2-D synthetic cases that display sharp hydraulic conductivity contrasts and specific connectivity patterns are investigated. These cases generate highly nonlinear objective functions that present multiple local minima. A derivative-free global optimization algorithm relying on a Gaussian process model and on the expected improvement criterion is used to efficiently localize the point of minimum of the objective functions, which corresponds to the contaminant source location. Even though concentration measurements contain a significant level of proportional noise, the algorithm efficiently localizes the contaminant source location. The variations of the objective function are essentially driven by the geology, followed by the design of the monitoring well network. The data and scripts used to generate objective functions are shared to favor reproducible research. This contribution is important because the functions present multiple local minima and are inspired from a practical field application. Sharing these complex objective functions provides a source of test cases for global optimization benchmarks and should help with designing new and efficient methods to solve this type of problem.
  • Publication
    Accès libre
    A pseudo genetic model of coarse braided-river deposits
    (2015-12)
    Pirot, Guillaume 
    ;
    Straubhaar, Julien 
    ;
    Renard, Philippe 
    A new method is proposed to produce three-dimensional facies models of braided-river aquifers based on analog data. The algorithm consists of two steps. The first step involves building the main geological units. The production of the principal inner structures of the aquifer is achieved by stacking Multiple-Point-Statistics simulations of successive topographies, thus mimicking the major successive flooding events responsible for the erosion and deposition of sediments. The second step of the algorithm consists of generating fine scale heterogeneity within the main geological units. These smaller-scale structures are generated by mimicking the trough-filling process occurring in braided rivers; the imitation of the physical processes relies on the local topography and on a local approximation of the flow. This produces realistic cross-stratified sediments, comparable to what can be observed in outcrops. The three main input parameters of the algorithm offer control over the proportions, the continuity and the dimensions of the deposits. Calibration of these parameters does not require invasive field measurements and can rely partly on analog data.
  • Publication
    Accès libre
    Influence of conceptual Model uncertainty on contaminant transport forecasting in braided river aquifers
    (2015-12)
    Pirot, Guillaume 
    ;
    Renard, Philippe 
    ;
    Huber, Emanuel
    ;
    Straubhaar, Julien 
    ;
    Huggenberger, Peter
    Hydrogeologist are commonly confronted to field data scarcity. An interesting way to compensate this data paucity, is to use analog data. Then the questions of prediction accuracy and uncertainty assessment when using analog data shall be raised. These questions are investigated in the current paper in the case of contaminant transport forecasting in braided river aquifers. In using analog data from the literature, multiple unconditional geological realizations are produced following different geological conceptual models (Multi-Gaussian, Object-based, Pseudo-Genetic). These petrophysical realizations are tested in a contaminant transport problem based on the MADE-II tracer experiment dataset. The simulations show that reasonable contaminant transport predictions can be achieved using analog data. The initial concentration conditions and location regarding the conductivity heterogeneity field have a stronger influence on the plume behavior than the resulting equivalent permeability. The results also underline the necessity to include a wide variety of geological conceptual models and not to restrain parameter space exploration within each concept as long as no field data allows for conceptual model or parameter value falsification.
  • Publication
    Accès libre
    Simulation of braided river elevation model time series with multiple-point statistics
    (2014-6-1)
    Pirot, Guillaume 
    ;
    Straubhaar, Julien 
    ;
    Renard, Philippe 
    A new method is proposed to generate successive topographies in a braided river system. Indeed, braided river morphology models are a key factor influencing river-aquifer interactions and have repercussions in ecosystems, flood risk or water management. It is essentially based on multivariate multiple-point statistics simulations and digital elevation models as training data sets. On the one hand, airborne photography and LIDAR acquired at successive time steps have contributed to a better understanding of the geomorphological processes although the available data are sparse over time and river scales. On the other hand, geostatistics provide simulation tools for multiple and continuous variables, which allow the exploration of the uncertainty of many assumption scenarios. Illustration of the approach demonstrates the ability of multiple-point statistics to produce realistic topographies from the information provided by digital elevation models at two time steps.
  • Publication
    Accès libre
    A practical guide to performing multiple-point statistical simulations with the Direct Sampling algorithm
    (2013-3)
    Meerschman, Eef
    ;
    Pirot, Guillaume 
    ;
    Mariethoz, Grégoire 
    ;
    Straubhaar, Julien 
    ;
    Van Meirvenne, Marc
    ;
    Renard, Philippe 
    The Direct Sampling (DS) algorithm is a recently developed multiple-point statistical simulation technique. It directly scans the training image (TI) for a given data event instead of storing the training probability values in a catalogue prior to simulation. By using distances between the given data events and the TI patterns, DS allows to simulate categorical, continuous and multivariate problems. Benefiting from the wide spectrum of potential applications of DS, requires understanding of the user-defined input parameters. Therefore, we list the most important parameters and assess their impact on the generated simulations. Real case TIs are used, including an image of ice-wedge polygons, a marble slice and snow crystals, all three as continuous and categorical images. We also use a 3D categorical TI representing a block of concrete to demonstrate the capacity of DS to generate 3D simulations. First, a quantitative sensitivity analysis is conducted on the three parameters balancing simulation quality and CPU time: the acceptance threshold t, the fraction of TI to scan f and the number of neighbors n. Next to a visual inspection of the generated simulations, the performance is analyzed in terms of speed of calculation and quality of pattern reproduction. Whereas decreasing the CPU time by influencing t and n is at the expense of simulation quality, reducing the scanned fraction of the TI allows substantial computational gains without degrading the quality as long as the TI contains enough reproducible patterns. We also illustrate the quality improvement resulting from post-processing and the potential of DS to simulate bivariate problems and to honor conditioning data. We report a comprehensive guide to performing multiple-point statistical simulations with the DS algorithm and provide recommendations on how to set the input parameters appropriately.
  • Publication
    Accès libre
    Distance-based kriging relying on proxy simulations for inverse conditioning
    (2013-1-10)
    Ginsbourger, David
    ;
    Rosspopoff, Bastien
    ;
    Pirot, Guillaume 
    ;
    Durrande, Nicolas
    ;
    Renard, Philippe