About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Novel Strategies for Rapid Acquisition and Processing of Large Datasets From Advanced Characterization Techniques
|
| Presentation Title |
High Throughput Texture Analysis of Quartz via Automated Polarized Reflective Light Microscopy |
| Author(s) |
Tekle Khmiadashvili, Brian Kendall, Katalyn Denby, Patrick Schools, Matteo Seita, Mengying Liu |
| On-Site Speaker (Planned) |
Mengying Liu |
| Abstract Scope |
Quartz is one of the most commonly found materials on the earth’s surface and analyzing its crystal texture aids in tectonic movement detection and earthquake prediction. With its uniaxial optical anisotropic property, under a white polarized light, grains with different orientations in quartz reflects various color and intensity. Rotating thin section samples on a dark background while taking micrographs of surface under a polarized reflective light microscope, we can generate the light intensity in red, green and blue channels reflected at each pixel. The periodically varied intensity and color can then be correlated to distinct grain’s c-axis orientation through a machine learning algorithm. This polarized reflective light microscopy particularly suitable for a larger field of view with numerous sub-millimeters size grains, offers a low-cost fast-screening option for rock texture analysis compared to Electron Backscatter Diffraction analysis. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Characterization, Machine Learning, |