Data from: Geo-SegNet: A contrastive learning enhanced U-Net for geomaterial segmentation

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.

Duke Scholars

Author Laura Elizabeth Dalton Civil and Environmental Engineering