Regular Article
Representative elementary volume estimation for porosity, moisture saturation, and air-water interfacial areas in unsaturated porous media: Data quality implications
Article first published online: 8 JUL 2011
DOI: 10.1029/2010WR009655
Copyright 2011 by the American Geophysical Union.
Additional Information
How to Cite
, , and (2011), Representative elementary volume estimation for porosity, moisture saturation, and air-water interfacial areas in unsaturated porous media: Data quality implications, Water Resour. Res., 47, W07513, doi:10.1029/2010WR009655.
Publication History
- Issue published online: 8 JUL 2011
- Article first published online: 8 JUL 2011
- Manuscript Accepted: 23 MAY 2011
- Manuscript Revised: 5 MAY 2011
- Manuscript Received: 14 JUN 2010
Keywords:
- REV;
- air-water interfacial area;
- microtomography;
- moisture saturation;
- representative elementary volume;
- unsaturated porous media
[1] Achieving a representative elementary volume (REV) has become a de facto criterion for demonstrating the quality of
CT measurements in porous media systems. However, the data quality implications of an REV requirement have not been previously examined. In this work, deterministic REVs for porosity, moisture saturation (SW), and air-water interfacial area (AI) were estimated using a set of 49
CT images of eight unsaturated homogeneous porous media with heterogeneity in moisture distributions present in varying degrees. Estimated porosity REVs were <8 mm3 for all cases, smaller than typical
CT image sizes (∼100 mm3). Estimated SW and AI REVs were <55 mm3 for cases with homogeneous moisture distributions but could not be estimated for cases with heterogeneous moisture distributions, due to the absence of a distinct “REV plateau” within the maximum imaged volume. Conventionally, SW and AI data from such non-REV cases would be excluded. The implications of excluding data on the basis of REV were examined by comparing AI-SW data measured on image windows of increasing size against the expected linear AI-SW relationship. At measurement scales exceeding porosity REV, random fluctuations in AI-SW data were excluded, even for cases containing heterogeneous moisture distributions. In contrast, requiring measurement scales to exceed SW and AI REV appeared overly restrictive and resulted in visible loss of reliable AI-SW data. We attribute these findings to overestimation of REVs due to inherently problematic estimation of deterministic REVs in real systems. Implications of these findings for ensuring
CT data quality and the efficient use of
CT data are discussed.

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