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Mariethoz, Grégoire

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Mariethoz, Grégoire
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https://libra.unine.ch/handle/123456789/941

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  • Publication
    Accès libre
    Fast and Interactive Editing Tools for Spatial Models
    (2018)
    Julien Straubhaar 
    ;
    Philippe Renard 
    ;
    Grégoire Mariethoz 
    ;
    Tatiana Chugunova
    ;
    Pierre Biver
    Multiple point statistics (MPS) algorithms allow generation of random fields reproducing the spatial features of a training image (TI). Although many MPS techniques offer options to prescribe characteristics deviating from those of the TI (e.g., facies proportions), providing a TI representing the target features as well as possible is important. In this paper, methods for editing stationary images by applying a transformation—painting or warping—to the regions, similar to a representative pattern selected by the user in the image itself, are proposed. Painting simply consists in replacing image values, whereas warping consists in deforming the image grid (compression or expansion of similar regions). These tools require few parameters and are interactive: the user defines locally how the image should be modified, then the changes are propagated automatically to the entire image. Examples show the ability of the proposed methods to keep spatial features consistent within the entire edited image.
  • Publication
    Accès libre
    Missing data simulation inside flow rate time-series using multiple-point statistics
    (2016-10)
    Oriani, Fabio 
    ;
    Borghi, Andrea 
    ;
    Straubhaar, Julien 
    ;
    Mariethoz, Grégoire 
    ;
    Renard, Philippe 
    The direct sampling (DS) multiple-point statistical technique is proposed as a non-parametric missing data simulator for hydrological flow rate time-series. The algorithm makes use of the patterns contained inside a training data set to reproduce the complexity of the missing data. The proposed setup is tested in the reconstruction of a flow rate time-series while considering several missing data scenarios, as well as a comparative test against a time-series model of type ARMAX. The results show that DS generates more realistic simulations than ARMAX, better recovering the statistical content of the missing data. The predictive power of both techniques is much increased when a correlated flow rate time-series is used, but DS can also use incomplete auxiliary time-series, with a comparable prediction power. This makes the technique a handy simulation tool for practitioners dealing with incomplete data sets.
  • Publication
    Accès libre
    Conditioning multiple-point statistics simulations to block data
    (2016-5)
    Straubhaar, Julien 
    ;
    Renard, Philippe 
    ;
    Mariethoz, Grégoire 
    Multiple-points statistics (MPS) allows to generate random fields reproducing spatial statistics derived from a training image. MPS methods consist in borrowing patterns from the training set. Therefore, the simulation domain is assumed to be at the same resolution as the conceptual model, although geometrical deformations can be handled by such techniques. Whereas punctual conditioning data corresponding to the scale of the grid node can be easily integrated, accounting for data available at larger scales is challenging. In this paper, we propose an extension of MPS able to deal with block data, i.e. target mean values over subsets of the simulation domain. Our extension is based on the direct sampling algorithm and consists to add a criterion for the acceptance of the candidate node scanned in the training image to constrain the simulation to block data. Likelihood ratios are used to compare the averages of the simulated variable taken on the informed nodes in the blocks and the target mean values. Moreover, the block data may overlap and their support can be of any shape and size. Illustrative examples show the potential of the presented algorithm for practical applications.
  • 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
    Simulation of rainfall time series from different climatic regions using the direct sampling technique
    (2014)
    Oriani, Fabio 
    ;
    Straubhaar, Julien 
    ;
    Renard, Philippe 
    ;
    Mariethoz, Grégoire 
    The direct sampling technique, belonging to the family of multiple-point statistics, is proposed as a nonparametric alternative to the classical autoregressive and Markov-chain-based models for daily rainfall time-series simulation. The algorithm makes use of the patterns contained inside the training image (the past rainfall record) to reproduce the complexity of the signal without inferring its prior statistical model: the time series is simulated by sampling the training data set where a sufficiently similar neighborhood exists. The advantage of this approach is the capability of simulating complex statistical relations by respecting the similarity of the patterns at different scales. The technique is applied to daily rainfall records from different climate settings, using a standard setup and without performing any optimization of the parameters. The results show that the overall statistics as well as the dry/wet spells patterns are simulated accurately. Also the extremes at the higher temporal scale are reproduced adequately, reducing the well known problem of overdispersion.
  • 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
    Conditioning Facies Simulations with Connectivity Data
    (2011-11)
    Renard, Philippe 
    ;
    Straubhaar, Julien 
    ;
    Caers, Jef
    ;
    Mariethoz, Grégoire 
    When characterizing and simulating underground reservoirs for flow simulations, one of the key characteristics that needs to be reproduced accurately is its connectivity. More precisely, field observations frequently allow the identification of specific points in space that are connected. For example, in hydrogeology, tracer tests are frequently conducted that show which springs are connected to which sink-hole. Similarly well tests often allow connectivity information in a petroleum reservoir to be provided. To account for this type of information, we propose a new algorithm to condition stochastic simulations of lithofacies to connectivity information. The algorithm is based on the multiple-point philosophy but does not imply necessarily the use of multiple-point simulation. However, the challenge lies in generating realizations, for example of a binary medium, such that the connectivity information is honored as well as any prior structural information (e.g. as modeled through a training image). The algorithm consists of using a training image to build a set of replicates of connected paths that are consistent with the prior model. This is done by scanning the training image to find point locations that satisfy the constraints. Any path (a string of connected cells) between these points is therefore consistent with the prior model. For each simulation, one sample from this set of connected paths is sampled to generate hard conditioning data prior to running the simulation algorithm. The paper presents in detail the algorithm and some examples of two-dimensional and three-dimensional applications with multiple-point simulations.
  • 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.
  • Publication
    Accès libre
    An Improved Parallel Multiple-Point Algorithm Using a List Approach
    (2011-4)
    Straubhaar, Julien 
    ;
    Renard, Philippe 
    ;
    Mariethoz, Grégoire 
    ;
    Froidevaux, Roland
    ;
    Besson, Olivier 
    Among the techniques used to simulate categorical variables, multiple-point statistics is becoming very popular because it allows the user to provide an explicit conceptual model via a training image. In classic implementations, the multiple-point statistics are inferred from the training image by storing all the observed patterns of a certain size in a tree structure. This type of algorithm has the advantage of being fast to apply, but it presents some critical limitations. In particular, a tree is extremely RAM demanding. For three-dimensional problems with numerous facies, large templates cannot be used. Complex structures are then difficult to simulate. In this paper, we propose to replace the tree by a list. This structure requires much less RAM. It has three main advantages. First, it allows for the use of larger templates. Second, the list structure being parsimonious, it can be extended to include additional information. Here, we show how this can be used to develop a new approach for dealing with non-stationary training images. Finally, an interesting aspect of the list is that it allows one to parallelize the part of the algorithm in which the conditional probability density function is computed. This is especially important for large problems that can be solved on clusters of PCs with distributed memory or on multicore machines with shared memory.
  • Publication
    Accès libre
    The Direct Sampling method to perform multiple-point geostatistical simulations
    (2010-11-20)
    Mariethoz, Grégoire 
    ;
    Renard, Philippe 
    ;
    Straubhaar, Julien 
    Multiple-point geostatistics is a general statistical framework to model spatial fields displaying a wide range of complex structures. In particular, it allows controlling connectivity patterns that have a critical importance for groundwater flow and transport problems. This approach involves considering data events (spatial arrangements of values) derived from a training image (TI). All data events found in the TI are usually stored in a database, which is used to retrieve conditional probabilities for the simulation. Instead, we propose to sample directly the training image for a given data event, making the database unnecessary. Our method is statistically equivalent to previous implementations, but in addition it allows extending the application of multiple-point geostatistics to continuous variables and to multivariate problems. The method can be used for the simulation of geological heterogeneity, accounting or not for indirect observations such as geophysics. We show its applicability in the presence of complex features, nonlinear relationships between variables, and with various cases of nonstationarity. Computationally, it is fast, easy to parallelize, parsimonious in memory needs, and straightforward to implement.