Nondestructive imaging of shallow buried objects using acoustic computed tomography
Bibliographic record
Abstract
The nondestructive three-dimensional acoustic tomography concept of the present investigation combines computerized tomography image reconstruction algorithms using acoustic diffracting waves together with depth information to produce a three-dimensional (3D) image of an underground section. The approach illuminates the underground area of interest with acoustic plane waves of frequencies 200-3000 Hz. For each transmitted pulse, the reflected-refracted signals are received by a line array of acoustic sensors located at a diametrically opposite point from the acoustic source line array. For a stratified underground medium and for a given depth, which is represented by a time delay in the received signal, a horizontal tomographic 2D image is reconstructed from the received projections. Integration of the depth dependent sequence of cross-sectional reconstructed images provides a complete three-dimensional overview of the inspected terrain. The method has been tested with an experimental system that consists of a line array of four-acoustic sources, providing plane waves, and a receiving line array of 32-acoustic sensors. The results indicate both the potential and the challenges facing the new methodology. Suggestions are made for improved performance, including an adaptive noise cancellation scheme and a numerical interpolation technique.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".