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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
    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.