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Straubhaar, Julien

Nom
Straubhaar, Julien
Affiliation principale
Site web
Email
julien.straubhaar@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/151
_
0000-0003-2718-4332

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  • Publication
    Accès libre
    A parsimonious parametrization of the Direct Sampling algorithm for multiple-point statistical simulations
    (2022)
    Juda, Przemyslaw 
    ;
    Renard, Philippe 
    ;
    Straubhaar, Julien 
    Multiple-point statistics algorithms allow modeling spatial variability from training images. Among these techniques, the Direct Sampling (DS) algorithm has advanced capabilities, such as multivariate simulations, treatment of non-stationarity, multi-resolution capabilities, conditioning by inequality or connectivity data. However, finding the right trade-off between computing time and simulation quality requires tuning three main parameters, which can be complicated since simulation time and quality are affected by these parameters in a complex manner. To facilitate the parameter selection, we propose the Direct Sampling Best Candidate (DSBC) parametrization approach. It consists in setting the distance threshold to 0. The two other parameters are kept (the number of neighbors and the scan fraction) as well as all the advantages of DS. We present three test cases that prove that the DSBC approach allows to identify efficiently parameters leading to comparable or better quality and computational time than the standard DS parametrization. We conclude that the DSBC approach could be used as a default mode when using DS, and that the standard parametrization should only be used when the DSBC approach is not sufficient.
  • Publication
    Accès libre
    Constraining distance-based multipoint simulations to proportions and trends
    (2015-10)
    Mariethoz, Grégoire 
    ;
    Straubhaar, Julien 
    ;
    Renard, Philippe 
    ;
    Chugunova, Tatiana
    ;
    Biver, Pierre
    In the last years, the use of training images to represent spatial variability has emerged as a viable concept. Among the possible algorithms dealing with training images, those using distances between patterns have been successful for applications to subsurface modeling and earth surface observation. However, one limitation of these algorithms is that they do not provide a precise control on the local proportion of each category in the output simulations. We present a distance perturbation strategy that addresses this issue. During the simulation, the distance to a candidate value is penalized if it does not result in proportions that tend to a target given by the user. The method is illustrated on applications to remote sensing and pore-scale modeling. These examples show that the approach offers increased user control on the simulation by allowing to easily impose trends or proportions that differ from the proportions in the training image.
  • Publication
    Accès libre
    3D multiple-point statistics simulation using 2D training images
    (2012-3)
    Comunian, Alessandro
    ;
    Renard, Philippe 
    ;
    Straubhaar, Julien 
    One of the main issues in the application of multiple-point statistics (MPS) to the simulation of three-dimensional (3D) blocks is the lack of a suitable 3D training image. In this work, we compare three methods of overcoming this issue using information coming from bidimensional (20) training images. One approach is based on the aggregation of probabilities. The other approaches are novel. One relies on merging the lists obtained using the impala algorithm from diverse 2D training images, creating a list of compatible data events that is then used for the MPS simulation. The other (s2Dcd) is based on sequential simulations of 2D slices constrained by the conditioning data computed at the previous simulation steps. These three methods are tested on the reproduction of two 3D images that are used as references, and on a real case study where two training images of sedimentary structures are considered. The tests show that it is possible to obtain 3D MPS simulations with at least two 2D training images. The simulations obtained, in particular those obtained with the s2Dcd method, are close to the references, according to a number of comparison criteria. The CPU time required to simulate with the method s2Dcd is from two to four orders of magnitude smaller than the one required by a MPS simulation performed using a 3D training image, while the results obtained are comparable. This computational efficiency and the possibility of using MPS for 3D simulation without the need for a 3D training image facilitates the inclusion of MPS in Monte Carlo, uncertainty evaluation, and stochastic inverse problems frameworks.
  • Publication
    Accès libre
    Extrapolating the Fractal Characteristics of an Image Using Scale-Invariant Multiple-Point Statistics
    (2011-10)
    Mariethoz, Grégoire 
    ;
    Renard, Philippe 
    ;
    Straubhaar, Julien 
    The resolution of measurement devices can be insufficient for certain purposes. We propose to stochastically simulate spatial features at scales smaller than the measurement resolution. This is accomplished using multiple-point geostatistical simulation (direct sampling in the present case) to interpolate values at the target scale. These structures are inferred using hypothesis of scale invariance and stationarity on the spatial patterns found at the coarse scale. The proposed multiple-point super-resolution mapping method is able to deal with "both continuous and categorical variables", and can be extended to multivariate problems. The advantages and limitations of the approach are illustrated with examples from satellite imaging.