Using Multiple-Point Geostatistics for Tracer Test Modeling in a Clay-Drape Environment with Spatially Variable Conductivity and Sorption Coefficient

Huysmans, Marijke; Orban, Philippe; Cochet, Elke; Possemiers, Mathias; Ronchi, Benedicta; Lauriks, Katherine; Batelaan, Okke; Dassargues, Alain

doi:10.1007/s11004-013-9502-1

Cited by 12 publications

(8 citation statements)

References 46 publications

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“…Similar algorithms include GROWTHSIM which introduces a random‐neighbor path [ Eskandari and Srinivasan , ] or HOSIM which uses spatial cumulants for pattern extraction [ Mustapha and Dimitrakopoulos , ]. Although training‐image based methods have been used in many hydrogeological applications [ Hermans et al ., ; Hu and Chugunova , ; Huysmans et al ., ; Mahmud et al ., ; Michael et al ., ], they suffer from limitations inherent to the simulation algorithms. Some important points that limit the applicability of these methods are a high computational cost, the difficulty to reproduce certain types of patterns, and most importantly the limited variability that can be recovered from a finite size training image [ Emery and Lantuéjoul , ].…”

Section: Introductionmentioning

confidence: 99%

Patch‐based iterative conditional geostatistical simulation using graph cuts

Mariethoz

et al. 2016

Water Resources Research

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Training image-based geostatistical methods are increasingly popular in groundwater hydrology even if existing algorithms present limitations that often make real-world applications difficult. These limitations include a computational cost that can be prohibitive for high-resolution 3-D applications, the presence of visual artifacts in the model realizations, and a low variability between model realizations due to the limited pool of patterns available in a finite-size training image. In this paper, we address these issues by proposing an iterative patch-based algorithm which adapts a graph cuts methodology that is widely used in computer graphics. Our adapted graph cuts method optimally cuts patches of pixel values borrowed from the training image and assembles them successively, each time accounting for the information of previously stitched patches. The initial simulation result might display artifacts, which are identified as regions of high cost. These artifacts are reduced by iteratively placing new patches in high-cost regions. In contrast to most patch-based algorithms, the proposed scheme can also efficiently address point conditioning. An advantage of the method is that the cut process results in the creation of new patterns that are not present in the training image, thereby increasing pattern variability. To quantify this effect, a new measure of variability is developed, the merging index, quantifies the pattern variability in the realizations with respect to the training image. A series of sensitivity analyses demonstrates the stability of the proposed graph cuts approach, which produces satisfying simulations for a wide range of parameters values. Applications to 2-D and 3-D cases are compared to state-of-the-art multiple-point methods. The results show that the proposed approach obtains significant speedups and increases variability between realizations. Connectivity functions applied to 2-D models transport simulations in 3-D models are used to demonstrate that pattern continuity is preserved.

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Section: Introductionmentioning

confidence: 99%

Patch‐based iterative conditional geostatistical simulation using graph cuts

Mariethoz

et al. 2016

Water Resources Research

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“…The existing methods to explore data features with heterogeneous variations are mainly from the perspective of the continuous spatiotemporal field. They can be roughly divided into the geostatistics analysis and statistical regression-based method (Huysmans et al, 2014). In geostatistics analysis, such as the Kriging (Kleijnen, 2009), generalized Kriging (Xu & Shu, 2015), Bayesian Maximum Entropy (Yu & Wang, 2013), etc., the spatiotemporal heterogeneous variation is considered where the temporal variation is a function of time distance and the spatial variation is a function of spatial distance, and the covariance function is used to describe the structure of heterogeneity (de Marsily et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

The Tensor‐based Feature Analysis of Spatiotemporal Field Data With Heterogeneity

et al. 2020

Earth and Space Science

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Heterogeneity is an essential characteristic of the geographic phenomenon. However, most existing researches concerning heterogeneity are based on the matrix. The bidimensional nature of the matrix cannot well support the multidimensional analysis of spatiotemporal field data. Here, we introduce an improved tensor‐based feature analysis method for spatiotemporal field data with heterogeneous variation, by utilizing the similarity measurement in multidimensional space and feature capture of tensor decomposition. In this method, the heterogeneous spatiotemporal field data are reorganized first according to the similarity and difference within the data. The feature analysis by integrating the spatiotemporal coupling is then obtained by tensor decomposition. Since the reorganized data have a more consistent internal structure than original data, the feature analysis bias caused by heterogeneous variation in tensor decomposition can be effectively avoided. We demonstrate our method based on the climatic reanalysis field data released by the National Oceanic and Atmospheric Administration. The comparison with conventional tensor decomposition showed that the proposed method can approximate the original data more accurately both in global and local regions. Especially in the area influenced by the complex modal aliasing and in the period time of the climatic anomaly events, the approximation accuracy can be significantly improved. The proposed method can also reveal the zonal variation of temperature gradient and abnormal variations of air temperature ignored in the conventional tensor method.

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“…The improved method was primarily used for reservoir phase pattern simulation (WU & FU, 2016;STRAUBHAAR et al, 2016). Simulations were mostly pattern simulations (VRIES et al, 2009;HUYSMANS et al, 2014), comprehensive simulations (LOU et al, 2015;GARDET et al, 2016), parameter optimization (GAO et al 2016), and computational efficiency research (ZUO et al, 2016;ABDOLLAHIFARD & NASIR, 2017). There are few studies on the patterns of continuous variables, and even fewer have been conducted on the calculation of resource reserves using the MPG method.…”

Section: Introductionmentioning

confidence: 99%