Three-dimensional voxel geological modelling for subsurface stratigraphy: a graph convolutional network approach
Bibliographic record
Abstract
Three-dimensional (3D) geological modelling enhances the understanding and visualisation of complex subsurface stratigraphy, which underpins geotechnical digital twin and resilience design. Existing methods for 3D geological modelling suffer from either high computational burden or low modelling accuracy in large-scale region with complex subsurface stratigraphy. This paper presents a novel deep learning method that applies the graph convolutional network to 3D voxel geological modelling using limited boreholes. A topological graph is firstly constructed, with spatial points encoded as graph nodes. The soil types and spatial coordinates are incorporated into the feature vector of each node. Spatial correlations are quantified through weighted edges by connecting pairs of nodes within a cuboid neighbouring system. Besides, the occurrence probability of soil types in all boreholes is embedded into the feature vector of each graph node to further improve the model robustness. A series of comparisons shows that the proposed method outperforms traditional two-point statistic and multi-point statistic methods in terms of modelling accuracy. The proposed method is finally applied to a real tunnel engineering in Changsha City, which demonstrates the effectiveness of the proposed method in complex 3D geological scenario.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.001 | 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".