UXO Time-Constant Estimation from Helicopter-Borne TEM Data
Bibliographic record
Abstract
Abstract We have developed a new helicopter-borne transient electromagnetic system, known as ORAGS-TEM, which was designed for the detection of unexploded ordnance (UXO) through very low altitude measurements. This system has already achieved considerable success in demonstrations over prepared test grids and a bombing site at the former Badlands Bombing Range (BBR) in South Dakota. UXO ranging in size from 113 kg (250 lb) bombs to 60 mm illumination shells and 7 cm (2.75 in) rocket components were detected by both magnetometer and transient electromagnetic technologies during these trials, conducted in September, 2002. The signal/noise ratio (SNR) observed in TEM measurements during these trials was high, prompting us to ask, “What degree of UXO discrimination can be achieved through detailed analysis of this airborne TEM dataset?” Given the degree of spatial averaging and sampling limitations imposed by the system's flight height and speed, we felt that a very detailed analysis of the type performed by Pasion and Oldenburg would not be practical. Instead, we developed an improved transient analysis technique based on the Matrix Pencil Method to improve the accuracy of exponential decomposition of the observed transients. Where target SNR values of 10 or higher were present this method yielded repeatable results that reliably distinguished compact, long-time-constant targets such as bombs and artillery shells from short-time-constant targets such as thin-walled scrap from practice bombs. As system sensitivity and resolution continues to improve, we expect that similar target discrimination methods will become standard data analysis tools.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".