Yang Zhang

To resolve the field-of-view versus resolution trade-off in micro-CT imaging of conglomerate reservoirs, Hy-MambaIR chains windowed self-attention with a Mamba state-space model into a local-to-global reconstruction network that recovers high-resolution pore and gravel structure from low-resolution wide-field slices. On paired multi-resolution micro-CT data it reaches a peak signal-to-noise ratio of 33.36 dB, 1.31 dB above HAT, and hits 30 dB with 6.6-fold fewer training iterations; on an 855³-voxel representative volume the mean relative error of three-directional absolute permeability is 18.56%, falling to 3.59% after supervised fine-tuning on pebbly sandstone.

To resolve the field-of-view versus resolution trade-off in micro-CT imaging of conglomerate reservoirs, Hy-MambaIR chains windowed self-attention with a Mamba state-space model into a local-to-global reconstruction network that recovers high-resolution pore and gravel structure from low-resolution wide-field slices. On paired multi-resolution micro-CT data it reaches a peak signal-to-noise ratio of 33.36 dB, 1.31 dB above HAT, and hits 30 dB with 6.6-fold fewer training iterations; on an 855³-voxel representative volume the mean relative error of three-directional absolute permeability is 18.56%, falling to 3.59% after supervised fine-tuning on pebbly sandstone.