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Title: Downscaling-based segmentation for unresolved images of highly heterogeneous granular porous samples

Abstract

Direct numerical simulations of pore-scale flow and transport in natural sediment columns require the accurate knowledge of pore-space topology. The limited resolution of X-ray tomography is often insufficient to fully characterize pore space structure within fine-grained regions. As a result, single and multilevel threshold-based segmentation approaches are customarily employed to identify solid, pore and porous solid regions in the image by means of grey intensity thresholds. While the choice of cutoff thresholds is arbitrary, it dramatically affects the effective properties as well as the dynamical response of the reconstructed porous structure. We propose a simple and efficient algorithm of downscaling followed by segmentation to reconstruct the unresolved pore space from X-ray computed tomography (XCT) images of natural geological porous media columns. Contrary to widely employed single and multilevel threshold-based segmentation, the proposed method is based on a continuous map between pixel intensity and local pixel porosity that does not rely on the definition of arbitrary thresholds. The approach is used to generate a high-resolution binary image of the porous medium from poorly resolved grey-scale images while preserving important sub-resolution information. First, we validate the method on a synthetic unresolved image of a heterogeneous porous medium and compare its known poremore » space distribution with the extracted one. The comparison shows that the method better represents the initial distribution than a comparable threshold-based segmentation and is more computationally efficient than stochastic reconstruction. Second, we apply the method to extract the pore space distribution from unresolved XCT images of two natural sediment columns, and use it to parametrize a capillary bundle model. The latter is then used to estimate the hydraulic conductivity and breakthrough behavior of passive solute transport. The comparison between model predictions and data is excellent and demonstrates that the algorithm is accurate across multiple length scales.« less

Authors:
 [1];  [2];  [2]; ORCiD logo [2];  [1]
  1. Stanford University
  2. BATTELLE (PACIFIC NW LAB)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1528962
Report Number(s):
PNNL-SA-125744
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Water Resources Research
Additional Journal Information:
Journal Volume: 54; Journal Issue: 4
Country of Publication:
United States
Language:
English

Citation Formats

Korneev, Svyatoslav, Yang, Xiaofan, Zachara, John M., Scheibe, Timothy D., and Battiato, Ilenia. Downscaling-based segmentation for unresolved images of highly heterogeneous granular porous samples. United States: N. p., 2018. Web. doi:10.1002/2018WR022886.
Korneev, Svyatoslav, Yang, Xiaofan, Zachara, John M., Scheibe, Timothy D., & Battiato, Ilenia. Downscaling-based segmentation for unresolved images of highly heterogeneous granular porous samples. United States. doi:10.1002/2018WR022886.
Korneev, Svyatoslav, Yang, Xiaofan, Zachara, John M., Scheibe, Timothy D., and Battiato, Ilenia. Sun . "Downscaling-based segmentation for unresolved images of highly heterogeneous granular porous samples". United States. doi:10.1002/2018WR022886.
@article{osti_1528962,
title = {Downscaling-based segmentation for unresolved images of highly heterogeneous granular porous samples},
author = {Korneev, Svyatoslav and Yang, Xiaofan and Zachara, John M. and Scheibe, Timothy D. and Battiato, Ilenia},
abstractNote = {Direct numerical simulations of pore-scale flow and transport in natural sediment columns require the accurate knowledge of pore-space topology. The limited resolution of X-ray tomography is often insufficient to fully characterize pore space structure within fine-grained regions. As a result, single and multilevel threshold-based segmentation approaches are customarily employed to identify solid, pore and porous solid regions in the image by means of grey intensity thresholds. While the choice of cutoff thresholds is arbitrary, it dramatically affects the effective properties as well as the dynamical response of the reconstructed porous structure. We propose a simple and efficient algorithm of downscaling followed by segmentation to reconstruct the unresolved pore space from X-ray computed tomography (XCT) images of natural geological porous media columns. Contrary to widely employed single and multilevel threshold-based segmentation, the proposed method is based on a continuous map between pixel intensity and local pixel porosity that does not rely on the definition of arbitrary thresholds. The approach is used to generate a high-resolution binary image of the porous medium from poorly resolved grey-scale images while preserving important sub-resolution information. First, we validate the method on a synthetic unresolved image of a heterogeneous porous medium and compare its known pore space distribution with the extracted one. The comparison shows that the method better represents the initial distribution than a comparable threshold-based segmentation and is more computationally efficient than stochastic reconstruction. Second, we apply the method to extract the pore space distribution from unresolved XCT images of two natural sediment columns, and use it to parametrize a capillary bundle model. The latter is then used to estimate the hydraulic conductivity and breakthrough behavior of passive solute transport. The comparison between model predictions and data is excellent and demonstrates that the algorithm is accurate across multiple length scales.},
doi = {10.1002/2018WR022886},
journal = {Water Resources Research},
number = 4,
volume = 54,
place = {United States},
year = {2018},
month = {4}
}