Attenuation of elastic waves in bentonite and monitoring of radioactive waste repositories
Bibliographic record
Abstract
Deep geological repositories, isolated from the geosphere by an engineered bentonite barrier, are currently considered the safest solution for high-level radioactive waste (HLRW) disposal. As the physical conditions and properties of the bentonite barrier are anticipated to change with time, seismic tomography was suggested as a viable technique to monitor the physical state and integrity of the barrier and to timely detect any unforeseen failure. To do so, the seismic monitoring system needs to be optimized, and this can be achieved by conducting numerical simulations of wave propagation in the repository geometry. Previous studies treated bentonite as an elastic medium, whereas recent experimental investigations indicate its pronounced viscoelastic behaviour. The aims of this contribution are (i) to numerically estimate the effective attenuation of bentonite as a function of temperature T and water content Wc, so that synthetic data can accurately reproduce experimental traces and (ii) assess the feasibility and limitation of the HLRW repository monitoring by simulating the propagation of sonic waves in a realistic repository geometry. A finite difference method was utilized to simulate the wave propagation in experimental and repository setups. First, the input of the viscoelastic model was varied to achieve a match between experimental and numerical traces. The routine was repeated for several values of Wc and T, so that quality factors Qp(Wc, T) and Qs(Wc, T) were obtained. Then, the full-scale monitoring procedure was simulated for six scenarios, representing the evolution of bentonite's physical state. The estimated Qp and Qs exhibited a minimum at Wc = 20 per cent and higher sensitivity to Wc, rather than T, suggesting that pronounced inelasticity of the clay has to be taken into account in geophysical modelling and analysis. The repository-model traces confirm that active seismic monitoring is, in principle, capable of depicting physical changes in the bentonite barrier. However, the locations of sources and receivers relative to the tunnel need to be optimized to achieve best sensitivity to the changes. It was also observed that first arrivals do not necessarily represent the most informative part of the signal; thus the time windows of the analysis need to be selected, accounting for the geometry of the acquisition system. This contribution should be considered as a next step towards the development of a numerical approach to aid in the design and optimization of a non-intrusive monitoring system in HLRW repositories.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".