Spatial Characterization of Shallow Structures in the Revell Batholith Integrating Seismic Imaging Techniques
Bibliographic record
Abstract
Abstract The Revell Site, located in Northwestern Ontario within the Canadian Shield, is being assessed as a potential Deep Geological Repository (DGR) for Canada’s used nuclear fuel. Effective DGR establishment requires comprehensive subsurface assessment, particularly in evaluating structural stability, hydrogeological attributes, geological composition, and geochemical properties. Key among these considerations is understanding the three-dimensional characteristics of structural features to ensure the site’s suitability for long-term containment of radioactive materials. This case study focuses on imaging and characterizing structures within the predominantly intact biotite granodiorite-tonalite host rock at the Revell Site. Borehole data reveals these structures as mostly sub-horizontal mafic intrusions, with thicknesses of up to 3.5 m. They appear either as separate, discrete entities or in stacked configurations. Despite their limited thickness, most of these intrusions exhibit discernible attributes in surface seismic images due to their distinct physical properties. Some, however, exhibit velocity variations that decrease their overall reflectivity properties. An integrative approach employing surface seismic, Vertical Seismic Profiles (VSP), and borehole data facilitates the spatial identification of over 30 of these mafic structures. This comprehensive characterization not only lays the foundational framework for future discrete fracture network models but also provides important support for simulations related to fluid flow dynamics, groundwater behavior, contaminant dispersion, and heat transport mechanisms within the Revell Site. This study underscores the critical role of in-depth subsurface characterization in ensuring the secure, long-term management of radioactive materials in DGRs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".