Classical approaches of feature line detection rely on curvature derivatives. They generally suffer from a common problem: the connectivity is hard to obtain and it is impossible to generate intersections between feature lines. This article presents a method to extract feature lines on 3D meshes. In order to sort out the recurrent issues of traditional approaches, we propose a novel algorithm based on two ideas. First, all the mesh vertices * Corresponding author. 531 Int. J. Image Grap. 2011.11:531-548. Downloaded from www.worldscientific.com by UNIVERSITY OF PITTSBURGH on 03/15/15. For personal use only. 532 D. Kudelski et al.are marked according to the curvature values: a binary map with candidate regions is then constructed. The second idea is to isolate each candidate region and transform it into a line. To achieve this, we parameterize the region into its 2D regular representation. We then perform a skeletonization to obtain lines with high connectivity. By applying the inverse parameterization, the feature lines are mapped back onto the 3D mesh. In the end, we extract perceptual salient parts and above all connected feature lines. In order to evaluate and validate our algorithm, we compare our method to classical ones and apply our technique to a geological context.
This study introduces a diagenetic modeling approach which is then applied to a simple one-dimensional scenario with an alternation of sandstone and shale layers. A key focus of the work is the analysis of the role of critical parameters that may drive pressure and overpressure dynamics in a basin. Due to its one-dimensional nature, the modeling technique presented can be currently applied as a Quality Control (QC) tool to assess the occurrence of diagenetic effects that might affect pressure evolution to assist in improving forecasting of overpressures in a new well in the same area or under similar geological settings. Coupling of hydro-geochemical and mechanical processes with the evolution of temperature enables one to model the effect of basin scale temperature-activated reactions on diagenetic scenarios. The resulting set of partial differential equations includes parameters whose values are always affected by high uncertainty. We provide uncertainty quantification (UQ) of the compaction process through a Global Sensitivity Analysis (GSA) of the system response following incomplete knowledge of a set of model parameters. The model response is approximated through a polynomial chaos expansion of the system dynamics. This decomposition provides (a) a GSA based on Sobol indices, and (b) a meta-model of the system that can then be adopted to perform multiple Monte Carlo realizations of the diagenetic process at an affordable computational time. Our results allow to (i) investigate the effect of parametric uncertainty on the system states, and (ii) perform robust parameter calibration within an inverse modeling framework. This work does not disclose significant data from the field or computer work; it contributes to improve our understanding and modeling of diagenesis of sandstones and shales and basin overpressure evolution. TX 75083-3836, U.S.A., fax +1-972-952-9435
Simulation software is a very common tool to model geomechanical problems since direct measurements are extremely expensive and usually unfeasible. In addition, there is an increasing interest in simulating past events and forecasting future ones. Very fine meshes are needed to provide a realistic representation of complex stratigraphy. Hence the full exploitation of modern HPC infrastructure is mandatory. In this work, a fully parallel GPU-accelerated simulator for extreme-scale models is presented and its performance is assessed through a real basin scale model that is used as benchmark.
Modern engineering applications require the solution of linear systems of millions or even billions of equations. The solution of the linear system takes most of the simulation for large scale simulations, and represent the bottleneck in developing scientific and technical software. Usually, preconditioned iterative solvers are preferred because of their low memory requirements and they can have a high level of parallelism. Approximate inverses have been proven to be robust and effective preconditioners in several contexts. In this communication, we present an adaptive Factorized Sparse Approximate Inverse (FSAI) preconditioner with a very high level of parallelism in both set-up and application. Its inherent parallelism makes FSAI an ideal candidate for a GPU-accelerated implementation, even if taking advantage of this hardware is not a trivial task, especially in the set-up stage. An extensive numerical experimentation has been performed on industrial underground applications. It is shown that the proposed approach outperforms more traditional preconditioners in challenging underground simulation, greatly reducing time-to-solution.
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