Inversion of time domain electromagnetic data for the detection of Unexploded Ordnance
Bibliographic record
Abstract
Unexploded Ordnance(UXO)discrimination is achieved by extracting parameters from geophysical data that reflect characteristics of the target that generated the measured signal. Model-based parameters are estimated through data inversion, where the optimal parameters are those that produce acceptable agreement between observed and predicted data and satisfy any prior information we have of the target. These parameters are then used as inputs to statistical classification methods to determine the likelihood that the target is, or is not, a UXO. The task of accurately recovering model parameters is more difficult when sensor data are contaminated with geological noise originating from magnetic soils. In regions of highly magnetic soil, magnetometry and electromagnetic sensors often detect large anomalies that are of geologic, rather than of metallic origin. In this thesis I investigate different methods of recovering the dipole polarization tensor from time domain electromagnetic (TEM) data. The different data inversion methods are characterized by the amount of a priori information used. Different a priori information considered include target location and depth estimated from other data sets, and knowledge of the different types of UXO that can be expected at the site. In the first part of this thesis, I assume that the influence of background geology can be removed through a data pre-processing procedures such that the UXO can be assumed to sit in free space. In the second part of this thesis we take a closer look at the influence of viscous remnant magnetization on electromagnetic data. Several software and hardware based approaches are proposed for improving detection and discrimination of UXO in geologically magnetic areas.
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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".