HexaShrink, an exact scalable framework for hexahedral meshes with attributes and discontinuities: multiresolution rendering and storage of geoscience models (@Patent inside)
Institution for moving co-authors: INRIA, ENS, PSL Research University Paris and HoloMake
Publications and references
@Article{Peyrot_J_2019_j-computat-geosci_hexashrink_pdgpcshmg,
author = {Peyrot, Jean-Luc and Duval, Laurent and Payan, Fr\'ed\'eric and Bouard, Lauriane and Chizat, L\'ena\"ic and Schneider, S\'ebastien and Antonini, Marc},
title = {{H}exa{S}hrink, an exact scalable framework for hexahedral meshes with attributes and discontinuities: multiresolution rendering and storage of geoscience models},
journal = {Computational Geosciences},
year = {2019},
volume = {23},
issue = {4},
month = {Aug.},
pages = {723--743},
doi = {10.1007/s10596-019-9816-2},
abstract = {With huge data acquisition progresses realized in the past decades and acquisition systems now able to produce high resolution grids and point clouds, the digitization of physical terrains becomes increasingly more precise. Such extreme quantities of generated and modeled data greatly impact computational performances on many levels of high-performance computing (HPC): storage media, memory requirements, transfer capability, and finally simulation interactivity, necessary to exploit this instance of big data. Efficient representations and storage are thus becoming ``enabling technologies'' in HPC experimental and simulation science (Foster \emph{et al.}, Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales, 2017). We propose {H}exa{S}hrink, an original decomposition scheme for structured hexahedral volume meshes. The latter are used for instance in biomedical engineering, materials science, or geosciences. {H}exa{S}hrink provides a comprehensive framework allowing efficient mesh visualization and storage. Its exactly reversible multiresolution decomposition yields a hierarchy of meshes of increasing levels of details, in terms of either geometry, continuous or categorical properties of cells.
Starting with an overview of volume meshes compression techniques, our contribution blends coherently different multiresolution wavelet schemes in different dimensions. It results in a global framework preserving discontinuities (faults) across scales, implemented as a fully reversible upscaling at different resolutions. Experimental results are provided on meshes of varying size and complexity. They emphasize the consistency of the proposed representation, in terms of visualization, attribute downsampling and distribution at different resolutions. Finally, {H}exa{S}hrink yields gains in storage space when combined to lossless compression techniques.},
file = {:Peyrot_J_2019_j-computat-geosci_hexashrink_pdgpcshmg.pdf:PDF},
keywords = {Compression; Corner point grid; Discrete wavelet transform; Geometrical discontinuities; Hexahedral volume meshes; High-Performance Computing (HPC); Multiscale methods; Simulation; Upscaling},
owner = {duvall},
timestamp = {2019.07.25-09.58},
}
With huge data acquisition progresses realized in the past decades and acquisition systems now able to produce high resolution point clouds, the digitization of physical terrains becomes increasingly more precise. Such extreme quantities of generated and modeled data greatly impact computational performances on many levels: storage media, memory requirements, transfer capability, and finally simulation interactivity, necessary to exploit this instance of big data. Efficient representations and storage are thus becoming "enabling technologies" in simulation science. We propose HexaShrink, an original decomposition scheme for structured hexahedral volume meshes. The latter are used for instance in biomedical engineering, materials science, or geosciences. HexaShrink provides a comprehensive framework allowing efficient mesh visualization and storage. Its exactly reversible multiresolution decomposition yields a hierarchy of meshes of increasing levels of details, in terms of either geometry, continuous or categorical properties of cells.
Starting with an overview of volume meshes compression techniques, our contribution blends coherently different multiresolution wavelet schemes. It results in a global framework preserving discontinuities (faults) across scales, implemented as a fully reversible upscaling. Experimental results are provided on meshes of varying complexity. They emphasize the consistency of the proposed representation, in terms of visualization, attribute downsampling and distribution at different resolutions. Finally, HexaShrink yields gains in storage space when combined to lossless compression techniques.
Keywords: Big data; Multiscale methods; Hexahedral volume meshes; Corner point grid; Discrete wavelet transform; Geometrical discontinuities; Compression; Upscaling; Reservoir modeling; Reservoir simulation; Meshes to meshes
Manipulation of large data volumes is becoming a concern in the field of simulation. In reservoir simulation, grids are complex structures composed of heterogeneous objects, with a potentially large number of cells. HexaShrink is a perfectly reversible multiscale decomposition tool based on 3D wavelet transformation, dedicated to structured hexahedral meshes. It provides a representation of mesh geometry and properties at different resolutions, coherently preserving discontinuities such as fault networks. This lossless compression method was initially designed for storage and transfer. Here we evaluate its suitability in the context of two-phase flow simulations. Initial results demonstrate comparable impacts of low-resolution grids on injected water. This suggests that embedding low resolution meshes can serve accelerated appromixate simulation results with little overhead, in a consistent upscaling and upgriding fashion.
Des méthodes de simulation employant des maillages sont mises en œuvre dans de nombreux champs scientifiques pour caractériser des phénomènes physiques sous-jacents. Les besoins croissants en précision induisent l'utilisation de maillages toujours plus volumineux, et entrainent des problèmes de visualisation, de manipulation ou de stockage des données. En géosciences, d'immenses zones géologiques sont modélisées par des maillages hexaédriques géométriquement complexes, émulant des propriétés physiques du sous-sol. En combinant différents types d'ondelettes classiques ou morphologiques qui préservent les discontinuités, HexaShrink (HS) apporte à cette problématique une solution multi-échelle cohérente. Dans cet article, nous analysons les performances de compression de HS, par l'étude exhaustive de sept maillages hétérogènes. Globalement, les taux de compression accrus, obtenus grâce à la décomposition, sont satisfaisants. Néanmoins, l'analyse distincte des différentes composantes définissant les maillages (géométrie, propriétés) révèle que l'utilisation d'encodeurs génériques n'est pas toujours optimale, ouvrant des perspectives vers des encodeurs multi-échelles plus à même d'exploiter les structures intrinsèques des niveaux de détail.
The invention is a method for exploitation of a sedimentary basin containing hydrocarbons, including optimized scaling of the geological model. Based on categorical property measurements, a first meshed representation of a formation is constructed reflecting the categorical property measurements. At least one second meshed representation having a lower resolution is constructed by assigning a categorical property value to each mesh of the second representation corresponding to a group of meshes of the first representation and storing parameters for changing from the second representation to the first representation with those change parameters enabling reconstitution of the first representation.
