The structural integrity of geological materials are closely tied to their porosity. Accurate knowledge of microspores in potential host rocks such as Opalinus Clay is essential for assessing their physical properties, including permeability and strength. Traditional methods for porosity analysis, such as mercury intrusion porosimetry (MIP) and gas pycnometry, provide valuable quantitative data on porosity and pore size distribution but do not offer insights into pore morphology or spatial distribution.
A methodological advancement comes with the combination of broad ion beam (BIB) milling and scanning electron microscopy (SEM), which allows for the visualization of pores at the nanoscale and facilitates detailed analysis of pore structures. However, segmenting pores from BIB-SEM images poses challenges due to the complexity of the images and the variability in pore shapes and sizes. This task is further complicated by the limited resolution of SEM and the subjective nature of manual pore identification.
To address these challenges, machine learning (ML) has emerged as a useful tool for automating the segmentation of pores from BIB-SEM images. We explore the use of conditional random fields (CRFs) as an ensemble method that improves segmentation by utilizing spatial and contextual information within the images. CRFs enhance segmentation accuracy and offer a robust framework for integrating results from multiple ML-classifiers. This probabilistic approach not only refines the segmentation accuracy but also enables the assessment of uncertainty levels in segmented pores, which is beneficial for accurately interpreting the microstructural properties.