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Renard, Philippe
Nom
Renard, Philippe
Affiliation principale
Fonction
Directeur de Recherche
Email
Philippe.Renard@unine.ch
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Résultat de la recherche
Voici les éléments 1 - 10 sur 153
- PublicationMétadonnées seulementMultiresolution Approach to Condition Categorical Multiple-Point Realizations to Dynamic Data With Iterative Ensemble Smoothing(2023-1-19)
; ; A new methodology is presented for the conditioning of categorical multiple-point statistics (MPS) simulations to dynamic data with an iterative ensemble smoother (ES-MDA). The methodology relies on a novel multiresolution parameterization of the categorical MPS simulation. The ensemble of latent parameters is initially defined on the basis of the coarsest-resolution simulations of an ensemble of multiresolution MPS simulations. Because this ensemble is non-multi-Gaussian, additional steps prior to the computation of the first update are proposed. In particular, the parameters are updated at predefined locations at the coarsest scale and integrated as hard data to generate a new multiresolution MPS simulation. The performance of the methodology was assessed on a synthetic groundwater flow problem inspired from a real situation. The results illustrate that the method converges towards a set of final categorical realizations that are consistent with the initial categorical ensemble. The convergence is reliable in the sense that it is fully controlled by the integration of the ES-MDA update into the new conditional multiresolution MPS simulations. Thanks to a massively reduced number of parameters compared to the size of the categorical simulation, the identification of the geological structures during the data assimilation is particularly efficient for this example. The comparison between the estimated uncertainty and a reference estimate obtained with a Monte Carlo method shows that the uncertainty is not severely reduced during the assimilation as is often the case. The connectivity is successfully reproduced during the iterative procedure despite the rather large distance between the observation points. - PublicationAccès libreA new perspective to model subsurface stratigraphy in alluvial hydrogeological basins, introducing geological hierarchy and relative chronology(2023-1-17)
- 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 libreIce volume and basal topography estimation using geostatistical methods and GPR measurements: Application on the Tsanfleuron and Scex Rouge glacier, Swiss Alps(2021-7)
; ; ; ; Ground Penetrating Radar (GPR) is nowadays widely used for determining glacier thickness. However, this method provides thickness data only along the acquisition lines and therefore interpolation has to be made between them. Depending on the interpolation strategy, calculated ice volumes can differ and can lack an accurate error estimation. Furthermore, glacial basal topography is often characterized by complex geomorphological features, which can be hard to reproduce using classical 5 interpolation methods, especially when the conditioning data are sparse or when the morphological features are too complex. This study investigates the applicability of multiple-point statistics (MPS) simulations to interpolate glacier bedrock topography using GPR measurements. In 2018, a dense GPR data set was acquired on the Tsanfleuron Glacier (Switzerland). The results obtained with the direct sampling MPS method are compared against those obtained with kriging and sequential Gaussian simulations (SGS) on both a synthetic data set – with known reference volume and bedrock topography – and the real data 10 underlying the Tsanfleuron glacier. Using the MPS modelled bedrock, the ice volume for the Scex Rouge and Tsanfleuron Glacier is estimated to be 113.9 ± 1.6 Miom3 . The direct sampling approach, unlike the SGS and the kriging, allowed not only an accurate volume estimation but also the generation of a set of realistic bedrock simulations. The complex karstic geomorphological features are reproduced, and can be used to significantly improve for example the precision of under-glacial flow estimation. - PublicationAccès libretTEM20AAR: a benchmark geophysical data set for unconsolidated fluvioglacial sediments(2021-6)
; - PublicationAccès libreAn Attempt to Boost Posterior Population Expansion Using Fast Machine Learning Algorithms(2021-3)
; In hydrogeology, inverse techniques have become indispensable to characterize subsurface parameters and their uncertainty. When modeling heterogeneous, geologically realistic discrete model spaces, such as categorical fields, Monte Carlo methods are needed to properly sample the solution space. Inversion algorithms use a forward operator, such as a numerical groundwater solver. The forward operator often represents the bottleneck for the high computational cost of the Monte Carlo sampling schemes. Even if efficient sampling methods (for example Posterior Population Expansion, PoPEx) have been developed, they need significant computing resources. It is therefore desirable to speed up such methods. As only a few models generated by the sampler have a significant likelihood, we propose to predict the significance of generated models by means of machine learning. Only models labeled as significant are passed to the forward solver, otherwise, they are rejected. This work compares the performance of AdaBoost, Random Forest, and convolutional neural network as classifiers integrated with the PoPEx framework. During initial iterations of the algorithm, the forward solver is always executed and subsurface models along with the likelihoods are stored. Then, the machine learning schemes are trained on the available data. We demonstrate the technique using a simulation of a tracer test in a fluvial aquifer. The geology is modeled by the multiple-point statistical approach, the field contains four geological facies, with associated permeability, porosity, and specific storage values. MODFLOW is used for groundwater flow and transport simulation. The solution of the inverse problem is used to estimate the 10 days protection zone around the pumping well. The estimated speed-ups with Random Forest and AdaBoost were higher than with the convolutional neural network. To validate the approach, computing times of inversion without and with machine learning schemes were computed and the error against the reference solution was calculated. For the same mean error, accelerated PoPEx achieved a speed-up rate of up to 2 with respect to the standard PoPEx. - PublicationAccès libreConditioning Multiple-Point Statistics Simulation to Inequality Data(2021-2)
; Stochastic modeling is often employed in environmental sciences for the analysis and understanding of complex systems. For example, random fields are key components in uncertainty analysis or Bayesian inverse modeling. Multiple-point statistics (MPS) provides efficient simulation tools for simulating fields reproducing the spatial statistics depicted in a training image (TI), while accounting for local or block conditioning data. Among MPS methods, the direct sampling algorithm is a flexible pixel-based technique that consists in first assigning the conditioning data values (so-called hard data) in the simulation grid, and then in populating the rest of the simulation domain in a random order by successively pasting a value from a TI cell sharing a similar pattern. In this study, an extension of the direct sampling method is proposed to account for inequality data, that is, constraints in given cells consisting of lower and/or upper bounds for the simulated values. Indeed, inequality data are often available in practice. The new approach involves the adaptation of the distance used to compare and evaluate the match between two patterns to account for such constraints. The proposed method, implemented in the DeeSse code, allows generating random fields both reflecting the spatial statistics of the TI and honoring the inequality constraints. Finally examples of topography simulations illustrate and show the capabilities of the proposed method. - PublicationAccès libreStatistical metrics for the characterization of karst network geometry and topology(2021-1)
; The authors regret that six of the statistical metrics published in Tables 2, 5, and 6 in Collon et al. (2017) are incorrect. The errors were discovered while coding and testing the open-source python package Karstnet freely available at https://github.com/karstnet/karstnet. The errors are smaller than 2%, except for the correlation of vertex degree, and they do not affect the general conclusions of the paper. The corrected tables are provided in this corrigendum, and we summarize below the changes - PublicationAccès libreAnalysis and stochastic simulation of geometrical properties of conduits in karstic networks(2020-11)
; Viseur, SophieDespite intensive explorations by speleologists, karstic systems remain only partially described as many conduits are not accessible to humans. The classical exploration techniques produce sparse data, leading to various uncertainties about the conduit dimensions, essential parameters for flow simulations. Stochastic simulations offer a possibility to better assess these uncertainties. In this paper, we propose different methods to stochastically simulate the properties (size and shape anisotropy) of karstic conduits on already existing skeletons. These approaches, based on Sequential Gaussian Simulations (SGS), allow taking different conditioning data into account, while respecting the intricacy of the networks. To infer the input parameters, we perform a statistical study of the conduit dimensions of 49 explored karstic networks, focusing on their equivalent radius and width-height ratio. Thanks to the definition of 1D curvilinear variograms, we demonstrate the existence of a spatial correlation along the networks, which is even higher when considering independently each conduit. Finally, using ad hoc algorithms implemented for computing both a conduit hierarchy inside karstic networks and a relative position regarding outputs, we find no evidence of an obvious link between these two entities and the studied metrics. The simulation methods are then demonstrated on the karstic network of Arrestelia (Pyrénées-Atlantiques, France), and show the consistency of the proposed approach with the observations made on the explored natural systems. - PublicationAccès libre3D Geological Image Synthesis from 2D Examples Using Generative Adversarial Networks(2020-10)
; Lefebvre, SylvainGenerative Adversarial Networks (GAN) are becoming an alternative to Multiple-point Statistics (MPS) techniques to generate stochastic fields from training images. But a difficulty for all the training image based techniques (including GAN and MPS) is to generate 3D fields when only 2D training data sets are available. In this paper, we introduce a novel approach called Dimension Augmenter GAN (DiAGAN) enabling GANs to generate 3D fields from 2D examples. The method is simple to implement and is based on the introduction of a random cut sampling step between the generator and the discriminator of a standard GAN. Numerical experiments show that the proposed approach provides an efficient solution to this long lasting problem.