An Improved Parallel Multiple-point Algorithm Using a List Approach

Straubhaar, Julien; Renard, Philippe; Mariethoz, Grégoire; Froidevaux, R.; Besson, Olivier

doi:10.1007/s11004-011-9328-7

Cited by 190 publications

(116 citation statements)

References 21 publications

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“…In theory, MPS implementations assume stationary TIs (Y. and MPS application to strongly non-stationary systems is currently an area of research (e.g. Honarkhah and Caers, 2012;Straubhaar et al, 2011;de Vries et al, 2009;Chugunova et al, 2008). The TI used for this study was generated by following the unique criterion that the unconditioned simulation (obtained by using SNESIM through its implementation in SGeMS) could satisfactorily reproduce the expected geological structures.…”

Section: Discussionmentioning

confidence: 99%

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

Høyer

Vignoli

Hansen

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Most studies on the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2-D or quasi-3-D training images. In the present study, we demonstrate a novel strategy for 3-D MPS modelling characterized by (i) realistic 3-D training images and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km 2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments) which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3-D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed spatial trends. The training image is constructed as a relatively small 3-D voxel model covering an area of 90 km 2 . We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, this study shows how to include both the geological environment and the type and quality of input information in order to achieve optimal results from MPS modelling. We present a practical workflow to build the training image and effectively handle different types of input information to perform large-scale geostatistical modelling.

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Section: Discussionmentioning

confidence: 99%

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

Høyer

Vignoli

Hansen

et al. 2017

Hydrol. Earth Syst. Sci.

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“…F can be dealt with in two ways. The first efficient MPS approaches -implemented for instance by Strebelle (2002) in SNESIM or by Straubhaar et al (2011Straubhaar et al ( , 2013 in IMPALA -rely on computing an histogram of the patterns present in the TI and compatible with the local data event d.Theseap-proaches are limited to categorical variables and with fixed search neighborhood templates. The use of successive multigrid simulation allows us, in that case, to capture and reproduce the structures from the TI at different scales while using small search templates,but may create some artifacts.…”

Section: The Direct Sampling An Mps Algorithmmentioning

confidence: 99%

“…If this is not the case, special techniques must be applied. Nonstationarity in the TI can be overcome by using probability maps or auxiliary variables (Chugunova and Hu, 2008;Boucher, 2009;de Vries et al, 2009) in classical MPS algorithms such as SNESIM (Strebelle, 2002) or IMPALA (Straubhaar et al, 2011). With the DS algorithm, auxiliary variables can be used as well to guide the spatial positioning of the topography structures (Mariethoz et al, 2010).…”

Section: Dem As Training Imagementioning

confidence: 99%

Simulation of braided river elevation model time series with multiple-point statistics

2014

Self Cite

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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.

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“…SNESIM uses multiple grids to reproduce large-scale structural information, thus efficiently improving the simulation quality. This has been adopted by several MPS algorithms, such as FILTERSIM (Zhang et al, 2006) and IMPALA (Straubhaar et al, 2011). Sensitivity analyses of the number of multiple grids (Liu, 2006;Strebelle, 2000) have revealed that it is critical to achieving acceptable simulation results.…”

Section: Introductionmentioning

confidence: 99%

Utilizing spatial association analysis to determine the number of multiple grids for multiple-point statistics

Bai

Mariethoz

2016

Spatial Statistics

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a b s t r a c tScale selection is a fundamental issue of spatial analysis. Based on spatial association analysis, this paper proposes a quantitative method for estimating the scale, which is represented using the minimal number of multiple grids in a single normal equation simulation. First, the largest scale structural information that a data template can consider for the finest grid is computed using the distance between the central cell and the border of the data template. The maximum distance at which cells are associated with each other in the training image is then analyzed using join count statistics. Finally, the minimal number of multiple grids is estimated based on the criterion that the data template used on the largest grid should account for the maximum distance between associated cells. The proposed method is validated using two-and three-dimensional experiments. The results show that increasing the number of multiple grids does not significantly improve the simulation quality when the number of multiple grids used is larger than that estimated. A sensitivity analysis demonstrates that the proposed method adapts to the configuration of the data template, 84H. Bai et al. / Spatial Statistics 17 (2016) the geometric structure of the target surface objects, and a rescaled training image if it adequately represents the large-scale structural information of the target surface objects.

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An Improved Parallel Multiple-point Algorithm Using a List Approach

Cited by 190 publications

References 21 publications

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

Simulation of braided river elevation model time series with multiple-point statistics

Utilizing spatial association analysis to determine the number of multiple grids for multiple-point statistics

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