Joint And Cooperative Inversion Of Magnetic And Time Domain Electromagnetic Data For The Characterization Of Uxo
Bibliographic record
Abstract
Magnetics and electromagnetic surveys are the primary techniques used for UXO remediation projects. Magnetometry is a valuable geophysical tool for UXO detection due to ease of data acquisition and its ability to detect relatively deep targets. However, magnetics data can have large false alarm rates due to geological noise, and there is an inherent non-uniqueness when trying to determine the orientation, size and shape of a target. Electromagnetic 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. In this paper we aim to improve discrimination ability by developing an interpretation method that takes advantage of the strengths of both techniques. We consider two different approaches to the problem: (1) Interpreting the data sets cooperatively, and (2) Interpreting the data sets jointly. For cooperative inversion information from the inversion of one data set is used as a constraint for inverting another data set. In joint inversion, target model parameters common to the forward solution of both types of data are identified and the model parameters from all the survey data are recovered simultaneously. We compare the confidence with which we can discriminate UXO from non-UXO targets when applying these different approaches to results from individual inversions. In this paper we focus on the details of the joint and cooperative inversion methodologies. Examples of the application of the methodology to TEM and magnetics data sets collected at the former Fort Ord in California are presented. This work is funded in part by the U.S. Army Engineer Research and Development Center and the Army Research Office.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".