Phantom subsurface targets in ground-penetrating radar data
Bibliographic record
Abstract
ABSTRACT Ground-penetrating radar (GPR) has been a very effective tool for exploring the subsurface and the nondestructive testing of nonmetallic structures for the past 40–50 years. The traditional GPR data interpretation is built upon the innate bias that all signals emanate from within the ground and most GPR users are normally under the impression that energy mostly travels straight down leading to the perception that “targets” are beneath the measurement location. The response of features at the ground surface and above ground also is present in most data but not always consciously noted as contributing to the measurements. One class of responses from above-ground features is routinely called “airwaves” because they normally exhibit moveout velocities of air. Often, an above-ground source is not the first thing that comes to mind during data interpretation, unless the user is experienced. Even experienced users can occasionally be misled, as above-ground features are expected to reach the GPR receiver with the moveout velocity of air. Recent experience in some of our surveys has created concerns because the targets at or above the ground surface demonstrated ground wave moveout velocity, which eliminates one of the diagnostic tools. This paper explores this issue, identifies GPR signal paths, and suggests key factors to consider in field operations and data interpretation. To demonstrate the concepts described, we have used numerical modeling and field data sets.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".