Workflow to numerically reproduce laboratory ultrasonic datasets
Bibliographic record
Abstract
The risks and uncertainties related to the storage of high-level radioactive waste (HLRW) can be reduced thanks to focused studies and investigations. HLRWs are going to be placed in deep geological repositories, enveloped in an engineered bentonite barrier, whose physical conditions are subjected to change throughout the lifespan of the infrastructure. Seismic tomography can be employed to monitor its physical state and integrity. The design of the seismic monitoring system can be optimized via conducting and analyzing numerical simulations of wave propagation in representative repository geometry. However, the quality of the numerical results relies on their initial calibration. The main aim of this paper is to provide a workflow to calibrate numerical tools employing laboratory ultrasonic datasets. The finite difference code SOFI2D was employed to model ultrasonic waves propagating through a laboratory sample. Specifically, the input velocity model was calibrated to achieve a best match between experimental and numerical ultrasonic traces. Likely due to the imperfections of the contact surfaces, the resultant velocities of P- and S-wave propagation tend to be noticeably lower than those a priori assigned. Then, the calibrated model was employed to estimate the attenuation in a montmorillonite sample. The obtained low quality factors (Q) suggest that pronounced inelastic behavior of the clay has to be taken into account in geophysical modeling and analysis. Consequently, this contribution should be considered as a first step towards the creation of a numerical tool to evaluate wave propagation in nuclear waste repositories.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.014 |
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".