Statistical fusion of two-scale images of porous media |
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| Authors: | Azadeh Mohebi Paul Fieguth Marios A Ioannidis |
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| Institution: | 1. Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada N2L 3G1;2. Department of Chemical Engineering, University of Waterloo, Waterloo, Canada N2L 3G1 |
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| Abstract: | 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. |
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| Keywords: | Porous media reconstruction Magnetic resonance imaging Data fusion Posterior sampling Simulated annealing Computed tomography |
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