materials

Supplementary materials to the following publication:

Strzelecki, P.J., Świerczewska, A., Kopczewska, K., Fheed, A., Tarasiuk, J., Wroński, S., 2021. Decoding Rocks: An Assessment of Geomaterial Microstructure Using X-ray Microtomography, Image Analysis and Multivariate Statistics. Materials 14, 3266. https://doi.org/10.3390/ma14123266

Abstract An understanding of the microstructure of geomaterials such as rocks is fundamental in the evaluation of their functional properties, as well as the decryption of their geological history. We present a semi-automated statistical protocol for a complex 3D characterization of the microstructure of granular materials, including the clustering of grains and a description of their chemical composition, size, shape, and spatial properties with 44 unique parameters. The approach consists of an X-ray microtomographic image processing procedure, followed by measurements using image analysis and statistical multivariate analysis of its results utilizing freeware and widely available software. The statistical approach proposed was tested out on a sandstone sample with hidden and localized deformational microstructures. The grains were clustered into distinctive groups covering different compositional and geometrical features of the sample’s granular framework. The grains are pervasively and evenly distributed within the analysed sample. The spatial arrangement of grains in particular clusters is well organized and shows a directional trend referring to both microstructures. The methodological approach can be applied to any other rock type and enables the tracking of microstructural trends in grains arrangement.
Keywords: microstructure; sandstone; deformation; X-ray microtomography; multivariate analysis; cluster analysis

Here you will find:

1. Publication text
2. Appendix B. The Code for Raw Data Processing and Data Set Preparation
3. Appendix D. The Code for Data Set Analysis
4. Appendix E. The Protocol Validation on a Standard Sample
5. Data Set

Visual representation of the clustering results presented as a fragment of the VOI (Fig.9b in the publication)