Using convolutional neural networks to classify unexploded ordnance from multicomponent electromagnetic induction data  
Bibliographic record
Abstract
Electromagnetic induction (EMI) methods are commonly used to classify unexploded ordnance (UXO) in both terrestrial and marine settings. Modern time-domain systems used for classification are multicomponent which means they acquire many transmitter-receiver pairs at multiple time-channels. Traditionally, classification is performed using a physics-based inversion approach where polarizability curves are estimated from the EMI data. These curves are then compared with those in a library to look for a match based on some misfit measure. In this work, we developed a convolutional neural network (CNN) that classifies UXO directly from EMI data. Analogous to an image segmentation problem, our CNN outputs a classification map that preserves the spatial dimensions of the input. In this way, our CNN produces high-resolution results and can handle the multiple transmitter-receiver pairs and the acquisition of multicomponent systems. We train the CNN using synthetic data generated with a dipole forward model considering relevant UXO and clutter objects. A careful design of the clutter classes is needed to maximize clutter discrimination. We use a two-step workflow. First, we train a CNN to detect metallic objects in field data. From this, we extract patches of data that contain only background signal and use these to generate a new training data set by adding this background noise to our synthetic data. A second CNN is trained with these data to perform the classification. We test our approach using field data acquired with the UltraTEMA-4 system in the Sequim Bay marine test site. Using this workflow, classification results for the field data show that our approach detects all of the UXOs and classifies more than 90% as the correct type while also discriminating ~70% of the clutter. A key advantage of our CNN is that, once trained, it may be used to provide real-time classification results on the field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.001 |
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".