Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
A variational level set approach to multiphase motion
Journal of Computational Physics
Digital Picture Processing
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Handwritten Character Recognition using the Continuous Distance Transformation
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Approximations of Shape Metrics and Application to Shape Warping and Empirical Shape Statistics
Foundations of Computational Mathematics
Image Processing - Principles and Applications
Image Processing - Principles and Applications
A new Hausdorff distance for image matching
Pattern Recognition Letters
A new image quality measure considering perceptual information and local spatial feature
GREC'09 Proceedings of the 8th international conference on Graphics recognition: achievements, challenges, and evolution
DGCI'06 Proceedings of the 13th international conference on Discrete Geometry for Computer Imagery
Energy minimization based segmentation and denoising using a multilayer level set approach
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
IEEE Transactions on Image Processing
Appropriate formulation of the objective function for the history matching of seismic attributes
Computers & Geosciences
Hi-index | 7.29 |
Reservoir engineers have to predict the behavior of a hydrocarbon reservoir by building a simulation model which can best reproduce the data collected in the field. These data fall into two types: static data, which are invariable in time, and dynamic data, which evolve according to fluid motions in the reservoir. In this paper, we focus on the integration of dynamic data related to four-dimensional (4D) inverted seismic data. Such seismic data constitute an invaluable source of information on fluid displacement and geology over extensive areas of the reservoir. However, incorporating them in the reservoir model through a matching process is a challenging task. Classical formulations of the objective function, which computes the misfit between observed data and responses computed by the reservoir model, are not adapted to 4D inverted seismic data. For example, a least square based mismatch is not representative of the visual difference between two seismic images. In this paper, we define a new formulation of the objective function based on simplification of seismic data in order to extract relevant information. This simplification involves filtering and segmentation techniques, as well as image comparison methods rooted in image analysis. More precisely, we focus on the non-local means algorithm for filtering, on the level-set framework for segmentation and on the local modified Hausdorff distance for image comparison. We investigate the efficiency of such techniques in the context of seismic data, and illustrate their potential on a synthetic history matching reservoir example.