AI-Assisted 3D Rock Fracture Characterization from Multi-View Smartphone Image–LiDAR Data in Underground Environments

Three-dimensional characterization of rock fractures in underground environments is challenging because restricted access, uneven illumination and partial visibility hinder mapping and structural measurements. This study presents an AI-assisted workflow that combines text-prompted Segment Anything Model 3 segmentation with smartphone image–LiDAR reconstruction to derive three-dimensional fracture supports. Candidate masks are combined into graded image evidence, transferred to the reconstructed surface and consolidated across views. Assessment combines manual image annotations, terres- trial laser scanning (TLS) surface and fracture references, field-compass measurements and conditional bootstrap analysis. On 25 annotated images, pixel-level precision and recall were 90.4% and 73.5%. Following alignment to TLS, block-level median absolute M3C2 surface-normal distances were 2.2–5.0 mm, while independent three-dimensional checkpoint RMSEs were 1.10–2.55 cm. For four fractures in a calcarenite chamber, full- image no-threshold supports covered 83.7–100% of the independent TLS traces within 5 cm. TLS plane discrepancies ranged from 0.3–32.7◦ without thresholding to 0.6–8.0◦ after thresholding, with fracture-specific coverage trade-offs. Controlled aggregation compar- isons showed no general advantage of weighting, while reduced-image characterization decreased coverage for all four fractures. The workflow links image evidence to indepen- dently assessed fracture geometry and orientation. Its structural reliability depends on the spatial distribution of the recovered support, motivating joint evaluation of recovery, plane agreement and resampling stability.