Preliminary studies of tunnel interface response modeling using test data from underground storage facilities.
Bibliographic record
Abstract
In attempting to detect and map out underground facilities, whether they be large-scale hardened deeply-buried targets (HDBT's) or small-scale tunnels for clandestine border or perimeter crossing, seismic imaging using reflections from the tunnel interface has been seen as one of the better ways to both detect and delineate tunnels from the surface. The large seismic impedance contrast at the tunnel/rock boundary should provide a strong, distinguishable seismic response, but in practice, such strong indicators are often lacking. One explanation for the lack of a good seismic reflection at such a strong contrast boundary is that the damage caused by the tunneling itself creates a zone of altered seismic properties that significantly changes the nature of this boundary. This report examines existing geomechanical data that define the extent of an excavation damage zone around underground tunnels, and the potential impact on rock properties such as P-wave and S-wave velocities. The data presented from this report are associated with sites used for the development of underground repositories for the disposal of radioactive waste; these sites have been excavated in volcanic tuff (Yucca Mountain) and granite (HRL in Sweden, URL in Canada). Using the data from Yucca Mountain, a numerical simulation effort was undertaken to evaluate the effects of the damage zone on seismic responses. Calculations were performed using the parallelized version of the time-domain finitedifference seismic wave propagation code developed in the Geophysics Department at Sandia National Laboratories. From these numerical simulations, the damage zone does not have a significant effect upon the tunnel response, either for a purely elastic case or an anelastic case. However, what was discovered is that the largest responses are not true reflections, but rather reradiated Stoneley waves generated as the air/earth interface of the tunnel. Because of this, data processed in the usual way may not correctly image the tunnel. This report represents a preliminary step in the development of a methodology to convert numerical predictions of rock properties to an estimation of the extent of rock damage around an underground facility and its corresponding seismic velocity, and the corresponding application to design a testing methodology for tunnel detection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".