A Three-Tier Evidence Hierarchy with Conditioned Generative Models for Discrete Fracture Network Reconstruction from Borehole Scanline Observations
Year: 2026
Type: preprint
Abstract: Fracture networks govern the mechanical behaviour of rock masses, and their accurate characterisation underpins slope stability, excavation design, and rock mass classification. Direct field observations sample only a small part of the fracture system, so much of the geometry remains unknown. This study presents an evidence-guided framework for reconstructing two-dimensional discrete fracture networks from sparse borehole scanline observations, organised as a three-tier hierarchy based on observational certainty. Measured traces are retained as Tier 1 anchors under a tolerance-based containment criterion, indicated traces are extended by a family-conditioned length predictor built around an Enhanced Variational Autoencoder, and inferred traces are generated for unobserved regions by a sequential transformer that learns the typical spatial layout of each joint family. All candidates pass through a multi-objective scorer that enforces seven geological constraints. The framework was validated on nine fully mapped bench faces of an open-pit mine, comprising 512 traces across three systematic joint families and random joints, at borehole spacings of 3 to 11 m. Relative to a Monte Carlo baseline, it reduced held-out per-family orientation error for all three joint families and improved the overall agreement with the mapped orientation and length distributions by about a third. Leave-one-bench-face-out testing confirmed that this performance reflects the framework rather than the particular faces held out. Reconstruction quality depended on a trade-off in observation spacing, with intermediate layouts giving the best overall balance across orientation accuracy, spatial fidelity, and network topology for the studied rock mass.
Institutions: UNSW Sydney, Shaoxing University
Language: English
Topic: Rock Mechanics and Modeling
Subfield: Mechanics of Materials
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7224742619