In-situ Primary Stress Detection Based on Seismic Tomography Measurements and Numerical Back-analysis for an Underground Radwaste Repository
Bibliographic record
Abstract
In order to understand how the in-situ primary stress state has evolved with subsidence and uplift in a granitic rock mass for anticipated of a radioactive waste repository in Hungary, the authors investigated the applicability of seismic tomography as an interpretive tool. Very high P wave velocity (Vp) values were obtained during the tomographic scanning of the study area of the repository, and these were compared with existing findings of in-situ and laboratory seismic measurements.Apart from seismic tomographic survey, dynamic FEM numerical modelling, empirical calculations of residual stresses, laboratory measurements of compression wave (ultrasonic) velocities on intact rock cores, in-situ primary stress measurements as well as site geological model were integrated to evaluate the use of seismic tomography for identifying possible in-situ stress increases around the excavation.A detailed calibration modelling was carried out based on the site seismic tomography measurements and during the large-scale modelling. It was observed that the increasing Vp is directly related to simulated increasing directional loadings on the rock mass. Using a measured wave raypath it was possible to check the different in-situ stress parametrizations which resulted in the best approximation to the measured Vp values.It was concluded that the rock mass under investigation to extend the repository must have higher in-situ stress values than the area of the constructed deposition chambers nearby. The results of this research indicated that seismic tomography is a useful tool for determining relative stress around and within the vicinity of underground excavation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".