Geoacoustic inversion for a 14-km autonomous underwater vehicle survey on the Malta Plateau
Bibliographic record
Abstract
We consider signal processing and inversion of 1487 source instances recorded on a towed array along a 14-km seabed survey on the Malta Plateau. The data were acquired by autonomous underwater vehicle (AUV) and processed as reflection coefficients versus grazing angle and frequency. Data acquisition caused several artifacts that are studied by various data representations. These representations expose periodic fluctuations of reflection coefficients with angle and frequency that do not depend on seabed location. Averaging over source instances, frequencies, and angles reduces the artifacts. The Bayesian inversion assumes a one-dimensional seabed structure for each data set and is parametrized by an unknown number of homogeneous layers, sound velocities, densities, and attenuations. Results from individual inversions and sequential Monte Carlo sampling are compared. We demonstrate that removing data artifacts reduces over-fitting problems from previous considerations of the same data. Comparisons to piston and gravity core estimates, and separate wide-angle data show good agreement with the AUV results for two locations along the track. However, results at greater depths exhibit high uncertainty and strong influence of chosen prior boundaries. When considering results for all 1487 data sets, dipping and terminating layers are found along the track with high resolution (∼10 cm).
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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 | 0.000 | 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".