Electromagnetic source localization in shallow waters using Bayesian matched-field inversion
Bibliographic record
Abstract
The propagation of an electromagnetic signal in a marine environment cannot be modelled as a plane wave due to the high attenuation in seawater and the interactions with the ocean boundaries. Consequently, conventional beamforming techniques are not applicable for electromagnetic source localization. In this work, the Bayesian approach to matched-field processing is used to localize an electromagnetic source and estimate the environmental parameters. In this formulation, the solution to the inverse problem is given by the a posteriori probability distribution calculated here using the Gibbs sampling method. Bayesian inversion theory provides the formalism for estimating parameters, their uncertainties and verification of the estimates convergence. Two situations were investigated for the case where the single frequency measurements represent the magnitudes of two orthogonal horizontal electric field components: (1) all environmental parameters known and (2) unknown seabed conductivity. The objective function that relates the array data to the propagation model and environment parameters was chosen for the practical situation considered.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".