In: Environmental Modelling & Software, 2015, vol. 72, p. 184-197
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...
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In: Advances in Water Resources, 2016, vol. 96, p. 145-169
Very different connectivity patterns may arise from using different simulation methods or sets of pa-rameters, and therefore different flow properties. This paper proposes a systematic method to compare ensemble of categorical simulations from a static connectivity point of view. The differences of static con-nectivity cannot always be distinguished using two point statistics. In addition,...
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In: Water Resources Research, 2017, vol. 53, p. 2902-2916
Solving inverse problems in a complex, geologically realistic, and discrete model space and from a sparse set of observations is a very challenging task. Extensive exploration by Markov chain Monte Carlo (McMC) methods often results in considerable computational efforts. Most optimization methods, on the other hand, are limited to linear (continuous) model spaces and the minimization of an...
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In: Water Resources Research, 2017, vol. 53, p. 8512-8532
The quantification of spatial rainfall is critical for distributed hydrological modeling. Rainfall spatial patterns generated by similar weather conditions can be extremely diverse. This variability can have a significant impact on hydrological processes. Stochastic simulation allows generating multiple realizations of spatial rainfall or filling missing data. The simulated data can then be used...
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In: Spatial Statistics, 2016, vol. 16, p. 53-71
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...
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In: Environmental Modelling & Software, 2016, vol. 86, p. 264-276
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...
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In: Hydrogeology Journal, 2015, vol. 23, no. 5, p. 883-900
The process of accounting for heterogeneity has made significant advances in statistical research, primarily in the framework of stochastic analysis and the development of multiple-point statistics (MPS). Among MPS techniques, the direct sampling (DS) method is tested to determine its ability to delineate heterogeneity from aerial magnetics data in a regional sandstone aquifer intruded by...
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In: Water Resources Research, 2015, vol. 51, no. 12, p. 9595-9611
A new method is proposed to produce three-dimensional facies models of braided-river aquifers based on analog data. The algorithm consists of two steps. The first step involves building the main geological units. The production of the principal inner structures of the aquifer is achieved by stacking Multiple-Point-Statistics simulations of successive topographies, thus mimicking the major...
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In: Journal of Hydrology, 2015, vol. 531, p. 124-141
Hydrogeologist are commonly confronted to field data scarcity. An interesting way to compensate this data paucity, is to use analog data. Then the questions of prediction accuracy and uncertainty assessment when using analog data shall be raised. These questions are investigated in the current paper in the case of contaminant transport forecasting in braided river aquifers. In using analog data...
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