Developing spatially constrained Discrete Fracture Network (DFN) models for a stochastic pit slope stability analysis
Bibliographic record
Abstract
In this paper, data from an open pit mine in Western Africa were used to generate spatially constrained large-scale DFN models. Structural data from geotechnical boreholes and borehole tele-viewers, drilled and logged in the pit area, were used to develop stochastic 3D block models of volumetric fracture intensity (P 32 ) using Sequential Gaussian Simulation. A geocellular 3D DFN model with finite volume of cellular grid elements were then created and spatially constrained based on the 3D block models of P 32 . This approach ensures that the fracture centers in the DFN generation are under the influence of cellular P 32 values at corresponding locations, estimated based on the 3D block model of volumetric fracture intensity. The joint orientations were bootstrapped based on the joint orientations observed along the drillholes. The geocellular DFN models were further calibrated using joint trace mapping data, collected across the pit area using a drone-based aerial photogrammetry. The resulting DFN models are expected to accurately reflect the spatial variation of fracture geometry and intensity along the pit area. Finally, 2D cross sections of the generated DFN models were incorporated into 2D finite element models of the pit slope for a stochastic slope stability analysis. The results of the stochastic modeling were compared to a simplistic approach assuming an average rock mass structural condition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".