Cooperative Inversion of Time Domain Electromagnetic and Magnetometer Data for The Discrimination of Unexploded Ordnance
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Bibliographic record
Abstract
Abstract Magnetic and electromagnetic induction (EMI) surveys are the primary techniques used for unexploded ordnance (UXO) remediation projects. Magnetometry is a valuable geophysical tool for UXO detection because of the ease of data acquisition and its ability to detect relatively deep targets. However, magnetic data can have large false alarm rates caused by geological noise, and there is an inherent non-uniqueness when trying to determine the orientation, size and shape of a target. EMI surveys, on the other hand, are relatively immune to geologic noise and are more diagnostic for target shape and size but have a reduced depth of investigation. We aim to improve discrimination ability by developing an interpretation method that takes advantage of the strengths of both techniques. We consider cooperative inversion, where information from the inversion of one type of data is used as a constraint for inverting another. We compare the confidence with which we can discriminate UXO from non-UXO targets when inverting the data sets cooperatively, to results from individual inversions. Examples are given of the application of the methodology to time domain electromagnetic induction (TEM) and magnetic data sets collected at the Yuma Proving Ground UXO Standardized Test Site calibration grid and the Former Camp Sibert.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it