Statistical fusion of two-scale images of porous media

• Published in: Advances in water resources Vol. 32; no. 11; pp. 1567 - 1579
• Main Authors: Mohebi, Azadeh, Fieguth, Paul, Ioannidis, Marios A.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.11.2009; Elsevier
• Subjects: Computed tomography; Data fusion; Earth sciences; Earth, ocean, space; Exact sciences and technology; groundwater flow; hydrologic models; Hydrology. Hydrogeology; Magnetic resonance imaging; Mathematical models; Porous media; Porous media reconstruction; Posterior sampling; Reconstruction; Samples; Simulated annealing; Statistical analysis; Statistical methods; Tasks; water flow; X-ray diffraction; Data fusion; Porous media reconstruction; Posterior sampling; Magnetic resonance imaging; Computed tomography; Simulated annealing; models; ground truth; sampling; fine-grained materials; X-rays; focus; annealing; resonance; matching; water resources; tomography; porous media; frame structure; high resolution
• ISSN: 0309-1708; 1872-9657
• DOI: 10.1016/j.advwatres.2009.08.005

The reconstruction of the architecture of void space in porous media is a challenging task, since porous media contain pore structures at multiple scales. Whereas past methods have been limited to producing samples with matching statistical behavior, the patterns of grey-level values in a measured sample actually say something about the unresolved details, thus we propose a statistical fusion framework for reconstructing high-resolution porous media images from low-resolution measurements. The proposed framework is based on a posterior sampling approach in which information obtained by low-resolution (MRI or X-ray) measurements is combined with prior models inferred from high-resolution microscopic data, typically 2D. In this paper, we focus on two-scale reconstruction tasks in which the measurements resolve only the large scale structures, leaving the small-scale to be inferred. The evaluation of the results generated by the proposed method shows the strong ability of the proposed method in reconstructing fine-scale structures positively correlated with the underlying ground truth. Comparing our method with the recent method of Okabe and Blunt [12], in which the measurements are also used in the reconstruction, we conclude that our method is more robust to the resolution of the measurement, and more closely matches the underlying fine-scale field.