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\title{An object-based approach to differentiate pores and microfractures in
petrographic analysis using explainable, supervised machine learning}



\author[1]{Issac Sujay Anand John Jayachandran}%
\author[1]{Holly Catherine Gibbs}%
\author[1]{Juan Carlos Laya}%
\author[2]{Yemna Qaiser}%
\author[2]{Talha Khan}%
\author[2]{Mohammed Ishaq Ansari}%
\author[2]{Mohammed Yaqoob Mohammed Shoeb Ansari}%
\author[2]{Mohammed Malyah}%
\author[3]{Nayef Alyafei}%
\author[2]{thomas Seers}%
\affil[1]{Texas A\&M University}%
\affil[2]{Texas A\&M University at Qatar}%
\affil[3]{Department of Petroleum Engineering, Texas A\&M University at Qatar}%


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  \date{May 07, 2026}


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\centering
\normalsize\begin{tabulary}{1.0\textwidth}{CCCCCCCCCCCC}
Study & Thin-sections & Segmented objects & Pore and microfracture classes (Train \# | Test \#) & Pixel resolution (microns/px) & Size features & Shape features & Features & ML models tested & Training/testing size & Sampling method & Testing accuracies (\%) \\
Ghiasi-Freez et al. (2012) & 24 & 384 & Interparticle (134 | 35), intraparticle (39 | 15), oomoldic (62 | 17), biomoldic (25 | 8), vuggy (34 | 15) & - & None & Elongation (aspect ratio), roundness, eccentricity, rectangularity, solidity, equivalent diameter/major axis diameter (they refer to as anisotropy) & 6 & Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) & Training = 294, testing = 90 & 16 largest pores from each sample & 66.6 - 100 \\
Borazjani et al. (2016) & 39 & 624 & Interparticle, intraparticle, moldic, intercrystalline, vuggy
(\emph{class proportions not provided})
 & - & Diameter (small, large), perimeter, area, ratio of area to porosity, equivalent diameter, bounding box area,  convex hull area & Aspect ratio, roundness, extent, solidity (referred to as stability) & 11 & Multi-Layer Perceptron (MLP) & Training = 624, testing = 0 & 16 largest pores from each sample & 70 - 100 \\
Mollajan et al. (2016) {[}\emph{same dataset as Ghiasi-Freez et al.,
2012}{]}
 & 24 & 384 & Interparticle (134 | 35), intraparticle (39 | 15), oomoldic (62 | 17), biomoldic (25 | 8), vuggy (34 | 15) & - & Equivalent diameter & Elongation (aspect ratio), roundness, eccentricity, rectangularity, solidity,  & 6 & Polynomial Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Radial Basis Function Neural  Network (RBF-NN), Fusion of all three & Training = 294, testing = 90 & 16 largest pores from each sample & 96.1 - 100 \\
Li et al. (2017) [microCT] & Not specifed & Not specified & Vugs and macrofractures & Unclear & Area, perimeter, equivalent circle diameter, effective length, tortuous length, equivalent width & Aspect ratio, shape factor, eccentricity & 10 & Support Vector Machine (SVM) & Not specified & Not specified & Not specified \\
Abedini et al. (2018) & 59 & 960 & Intraparticle (509 | 197), vuggy (37 | 9), moldic (20 | 12), biomoldic (5 | 3), interparticle (103 | 50), microfracture (8 | 7) & - & Area, major axis length of ellipse, minor axis length of ellipse, mean diameter, mean feret diameter, length, width & Extent, aspect ratio (ellipse), aspect ratio (rectangle), radius ratio, roundness, & 13 & Back-propagation Network (BPN), Stacked Autoencoder (SAE) & Training = 682, testing = 278 & Not provided & 94.6 - 96.04 \\
Singh et al. (2021) [microCT] & 4 & 12069 - 117270 & Pores and macrofractures & 4.52-11.49 & Perimeter, spatial moments, central moments & Aspect ratio (ellipse), aspect ratio (rectangle), spatial moments, central moments & 20 & Projection-based clustering using Principal Components Analysis (PCA) and k-means & Not provided & Not provided & 89.4 - 95.5 \\
Sharifi (2022) & 160 & 250 & Intraparticle, intracrystal, vuggy, moldic, interparticle,
microporosity, intercrystalline, microfracture (\emph{class proportions
not provided})
 & - & Equivalent diameter & Aspect ratio, roundness, eccentricity, rectangularity, solidity & 6 & Poly SVM, kNN, RBF-NN, Fusion of all three, MLP & Training = 190, testing = 60 & Manually curated from randomly selected thin sections & N/A \\
Wang et al. (2022) [microCT] & - & - & Pores and microfractures & - & Maximum Feret diameter, minimum Feret diameter, major axis length of fitted ellipse, minor axis length of fitted ellipse & Circularity, aspect ratio (ellipse), solidity, tortuosity & 8 & Decision tree (DT) & - & Manually curated & [?]100 \\
This study & 18 & 20060 & Pores (280 | 120) and microfractures (280 | 120) & 6.35 & None & Aspect ratio, roundness, compactness, circularity, extent, formfactor, solidity & 7 & Multiple Logistic Regression (MLR), LDA, QDA, kNN, Na\selectlanguage{ngerman}ïve-Bayes (NB), Random Forest (RF), SVM (3 variants) & Training = 560, testing = 240 & Manual for microfractures, pseudo-random sampling for pores & 89.58 - 90.83 \\
\end{tabulary}
\caption{{\href{https://github.com/issacsujay92/Microfractures-And-Pores-ML}{Code}\href{https://doi.org/10.7910/DVN/T2LESU}{Image
Data and results}
{\label{847443}}%
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\textbf{Dataset:}~\url{https://doi.org/10.7910/DVN/T2LESU}
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