Spatial variability of seabed properties on the Malta Plateau inferred with an autonomous underwater vehicle
Bibliographic record
Abstract
We develop an automated method to infer geoacoustic properties along tracks surveyed by autonomous underwater vehicles (AUVs). The AUV tows a 32-element receiver array and a source emitting signals (“pings”) at regular intervals. Recordings are processed in terms of reflection coefficients, resulting in large data volumes with substantively more information on seabed structure than traditional seismic profiling. However, interpreting seabed spatial variability requires efficient inversion. The inverse problem is non-linear and requires Bayesian sampling to quantify parameter uncertainties. To account for changes in the number of seabed layers at each ping position, the parametrization treats this number as unknown with a Poisson prior and even-numbered order statistics to improve efficiency. The method is applied to 340 data sets along a 14-km track on the Malta Plateau, employing 8 graphics processing units for approximately 2 weeks of computing time. The results resolve layering along the track with previously unreported detail. An erosional boundary is clearly resolved as a high-velocity, high-density layer and appears rougher and is buried deeper in shallower water. Depressions along this boundary are filled in with lower-velocity material. In addition, sound attenuation is well constrained in a thick low-velocity wedge. [Work supported by ONR and SERDP.]
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".