Enhancement of Magnetic Data by Stable Downward Continuation for UXO Application
Bibliographic record
Abstract
The magnetic method has been proven to be a successful geophysical tool for the detection of unexploded ordnance (UXO). Aeromagnetic surveys are advantageous since they can acquire data over large areas. The downside is that magnetic anomalies due to multiple metallic targets can overlap significantly due to flight height restrictions. Such overlap combined with the acquisition noise may significantly decrease the signal-to-noise ratio of data. These adverse effects can mask the true level of contamination at a site during the initial assessment based on the magnetic method as well as decrease the overall effectiveness of discrimination during the active clearance stage. We propose a method to ameliorate these difficulties using stable downward continuation, which reconstructs the field at a lower observation height from the observed data. The stable algorithm formulates the downward continuation as an inverse problem and incorporates the expected power spectrum of UXO anomalies. The power spectrum preserves the spectral properties and subdues the amplification of high-frequency noise. Synthetic and field examples show that the algorithm can reliably reconstruct the magnetic anomaly at the ground surface within the limitation imposed by the noise. The reconstructed field exhibits significant enhancement compared to the original data.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".