From μCT images to micro scale parameters: A deep learning workflow for three-phase segmentation and 3D quantitative analysis of submarine landslide sediment

Submarine landslides are a major geohazard, often preconditioned by micro-structural heterogeneity within sensitive weak layers. While X-ray micro-computed tomography (μCT) offers high-resolution insights into these sediments, quantifying phase distributions remains a bottleneck due to the complexity of manual segmentation. This study establishes an end-to-end deep learning workflow for the automated three-phase segmentation and 3D quantitative analysis of submarine sediments. Specifically, we compared two architectures, U-Net and DeepLabV3+, on high-resolution μCT scans of slope sediments from Finneidfjord (Norway) and the AFEN slide (UK). Both models achieved high segmentation accuracy (Dice > 0.84). DeepLabV3+ demonstrated superior computational efficiency and generalization for gas phases, while U-Net proved more robust in resolving subtle liquid–solid interfaces crucial for pore-scale mechanics. To evaluate model robustness and generalization, both approaches were tested across contrasting sedimentary endmembers, including homogeneous background sediments and micro-structurally heterogeneous weak-layer sequences relevant for slope failure initiation. The automated 3D reconstruction revealed a specific "coarse-grain framework with fine-grain infill" texture in the Finneidfjord weak layer, suggesting that obstructed drainage pathways may facilitate excess pore pressure accumulation. Ultimately, this study establishes an AI-driven workflow for high-throughput quantitative μCT analysis, enabling the identification of micro-mechanical precursors associated with slope failure.