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Renard, Philippe
Nom
Renard, Philippe
Affiliation principale
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Directeur de Recherche
Email
Philippe.Renard@unine.ch
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Voici les éléments 1 - 10 sur 13
- PublicationAccès libreEfficiency of template matching methods for Multiple-Point Statistics simulations(2021-8)
; Almost all Multiple-Point Statistic (MPS) methods use internally a template matching method to select patterns that best match conditioning data. The purpose of this paper is to analyze the performances of ten of the most frequently used template matching techniques in the framework of MPS algorithms. Performance is measured in terms of computing efficiency, accuracy, and memory usage. The methods were tested with both categorical and continuous training images (TI). The analysis considers the ability of those methods to locate rapidly and with minimum error a data event with a specific proportion of known pixels and a certain amount of noise. Experiments indicate that the Coarse to Fine using Entropy (CFE) method is the fastest in all configurations. Skipping methods are efficient as well. In terms of accuracy, and without noise all methods except CFE and cross correlation (CC) perform well. CC is the least accurate in all configurations if the TI is not normalized. This method performs better when normalized training images are used. The Binary Sum of Absolute Difference is the most robust against noise. Finally, in terms of memory usage, CFE is the worst among the ten methods that were tested; the other methods are not significantly different. - PublicationAccès libreThe Traveling Pilot Point method. A novel approach to parameterize the inverse problem for categorical fields(2020-2)
; ; Doherty, JohnCategorical parameter distributions are common-place in hydrogeological systems consisting of geologic fa cies/categories with distinct properties, e.g., high-permeability channels embedded in a low-permeability ma trix. Parameter estimation is difficult in such systems because the discontinuities in the parameter space hinder the inverse problem. Previous research in this area has been focused on the use of stochastic methods. In this paper, we present a novel approach based on Traveling Pilot points (TRIPS) combined with subspace parameter estimation methods to generate realistic categorical parameter distributions that honor calibration constraints (e.g., - measured water levels). In traditional implementations, aquifer properties (e.g., hydraulic conductivity) are estimated at fixed pilot point locations. In the TRIPS implementation, both the properties associated with the pilot points and their locations are estimated. Tikhonov regularization constraints are incorporated in the param eter estimation process to produce realistic parameter depictions. For a synthetic aquifer system, we solved the categorical inverse problem by combining the TRIPS methodology with two subspace methods: Null Space Monte Carlo (NSMC) and Posterior Covariance (PC). A posterior ensemble developed with the rejection sampling (RS) method is compared against the TRIPS ensembles. The comparisons indicated similarities between the various ensembles and to the reference parameter distribution. Between the two subspace methods, the NSMC method produced an ensemble with more variability than the PC method. These preliminary results suggest that the TRIPS methodology has promise and could be tested on more complicated problems. - PublicationAccès libreMissing data simulation inside flow rate time-series using multiple-point statistics(2016-10)
; ; ; ; 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. - PublicationAccès libreConditioning multiple-point statistics simulations to block data(2016)
; 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. - PublicationAccès libreIntegrating aerial geophysical data in multiple-point statistics simulations to assist groundwater flow models(2015-10)
; ; - PublicationAccès libreSimulation of braided river elevation model time series with multiple-point statistics(2014-6-1)
; ; 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. - PublicationAccès libreA practical guide to performing multiple-point statistical simulations with the Direct Sampling algorithm(2013-3)
; ; ; - PublicationAccès libreParallel Multiple-point Statistics Algorithm Based on List and Tree Structures(2013-2)
; - PublicationAccès libreConditioning Facies Simulations with Connectivity Data(2011-11)
; ; - PublicationAccès libreThree-dimensional high resolution fluvio-glacial aquifer analog - Part 2: Geostatistical modeling(2011-7-21)
; ; Bayer, PeterThe heterogeneity of sedimentary structures at the decimeter scale is crucial to the understanding of groundwater flow and transport. In a series of two papers, we provide a detailed analysis of a fluvio-glacial aquifer analog: the Herten site. The geological data along a series of 2D sections in a quarry, the corresponding GPR measurements, and their sedimentological interpretation are described in the companion paper. In this paper, we focus on the three-dimensional reconstruction of the heterogeneity. The resulting numerical model is provided as an electronic supplementary material for further studies. Furthermore, the geostatistical parameters derived from this analysis and the methodology described in the paper could be used in the future for the simulation of similar deposits where less data would be available. To build the 3D model, we propose a hierarchical simulation method which integrates various geostatistical techniques. First, we model the subdivision of the domain into regions corresponding to main sedimentological structures (e.g. a sedimentation event). Within these volumes, we use multiple-point statistics to describe the internal heterogeneity. What is unusual here is that we do not try to use a complex training image for the multiple-point algorithm accounting for all the non-stationarity and complexity, but instead use a simple conceptual model of heterogeneity (ellipsoidal shapes as a training image) and constrain the multiple point simulations within the regions by a detailed interpolation of orientation data derived from the 2D sections. This method produces realistic geological structures. The analysis of the flow and transport properties (hydraulic conductivity and tracer breakthrough curves) of the resulting model shows that it is closer to the properties estimated directly from the 2D geological observations rather than those estimated from a model of heterogeneity based on probability of transitions and not including the modeling of the large-scale structures.