- The dataset was obtained using high-resolution X-ray micro-CT scans with a TESCAN UniTOM XL scanner at Duke University’s Creativ Engineering Laboratory. Samples were secured with a custom core holder featuring a magnetic aluminum base and stabilized using an acrylic tube. Scanning parameters included 160 kV voltage, 20 W power, 4096 projections, 20 µm voxel size, and a 1.5 mm copper filter. The UniTOM XL’s helical scanning captured full core widths, and 9266 representative slices were selected to optimize computation and train models on pore feature segmentation.
- Total Size
- 13 files (62.3 GB)
- Data Citation
- Tian, Q., Goodhue, S., Xiong, H., & Dalton, L. E. (2024). Data from: Geo-SegNet: A contrastive learning enhanced U-Net for geomaterial segmentation. Duke Research Data Repository. https://doi.org/10.7924/r4rf5zz3v
- DOI
- 10.7924/r4rf5zz3v
- Publication Date
- December 13, 2024
- ARK
- ark:/87924/r4rf5zz3v
- Affiliation
- Publisher
- Type
- Contact
- Qinyi Tian: qinyi.tian@duke.edu
- Laura Dalton: laura.dalton@duke.edu, https://orcid.org/0000-0002-3230-8128
- Title
- Data from: Geo-SegNet: A contrastive learning enhanced U-Net for geomaterial segmentation
- Repository