Pore size distributions convolution for microtomographic images applied to Shark Bay's oolite

Area of Science:

• Geology
• Materials Science
• Image Analysis

Background:

• Characterizing pore size distribution is crucial for understanding material properties.
• Current methods lack consensus on integrating data from varying microtomographic resolutions.
• Accurate pore characterization is vital for geological and material science applications.

Purpose of the Study:

• To propose a novel method for grouping pore size distributions from microtomography at different spatial resolutions.
• To develop a unified pore size distribution for a more representative sample characterization.
• To validate the proposed method by comparing results with mercury porosimetry.

Main Methods:

• X-ray microtomography imaging of Shark's Bay oolite at 4.4μm and 1.1μm spatial resolutions.
• Determination of pore size distribution and average porosity for each resolution.
• Development of a weighting factor based on pore counts to merge distributions.
• Calculation of average porosity from the grouped pore size distribution.

Main Results:

• Pore size distributions were successfully obtained at two distinct spatial resolutions.
• A weighting factor was derived to effectively combine these distributions into a single, unified distribution.
• The grouped pore size distribution yielded an average porosity value comparable to mercury porosimetry measurements.
• The integrated distribution provided a more representative characterization of the sample's pore structure.

Conclusions:

• The proposed method offers a robust solution for integrating pore size data from varying microtomographic resolutions.
• This approach enhances the representativeness of pore structure characterization in geological samples.
• The findings suggest broader applicability for this technique in materials science and porous media research.