Bibliographic record
Abstract
The presence of subsurface anomalies, such as cavities, faults, unknown tunnels, etc., either natural or man-made, can cause public safety hazards. The detection of these features requires the development of new methods. Seismic Rayleigh surface wave imaging is a relatively new non-destructive testing technique (NDT) which generates subsurface images without drilling boreholes into the ground, and in recent years has been widely used for soil characterization in geotechnical investigations. In the last decade, some researchers have applied the technique to near-surface imaging and showed the possibility and potential for engineering applications.\n\nThis research presents the development of a technique to process seismic Rayleigh waves to detect and image subsurface anomalies. This study conducted investigations of Rayleigh wave behaviors and developed a new strategy for Rayleigh wave isolation from raw field data. The strategy applies wavelet transforms, instead of the conventional spectral analysis of surface waves (SASW) method, or popular multichannel analysis of surface waves (MASW) techniques, to pair-channel analysis of the isolated Rayleigh wave data for dispersion calculation. Finally, a simple steady inversion technique was applied to yield shear velocity as a function of both depth and distance, and shear velocity field images (SVF), for near surface section display. \n\nThis research consists of development, computer programming, field tests, data processing and interpretation. Three sites in different scenarios were used for seismic investigations: old mining tunnels in medium dipping coal seams in Stellarton coalfield, mining cavities in steeply dipping gold-bearing veins in West Waverley Gold District and an anomaly in nearly horizontal strata in Liverpool. All these sites are in the province of Nova Scotia, Canada. The results from seismic surface wave technique introduced in this research can be evaluated by field observations, documents and borehole logs. The satisfactory interpretations and success of this investigation shows that this technique is suitable for engineering application for subsurface investigations.
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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 | 0.001 | 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 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".