Geoacoustic inversion to study spatial variability and uncertainty along a 14-km seabed survey on the Malta Plateau
Bibliographic record
Abstract
Seabed variability of continental shelves is well understood at kilometer and centimeter scales; however, mesoscales of several meters are poorly understood. While vertical seismic profiling can provide insights into layer geometries, geoacoustic parameter values are not estimated, and structural images are averaged over ~100 m and typically distorted. Here, we apply automated sequential inversion to seabed reflectivity data recorded using an autonomous underwater vehicle (AUV) on the Malta Plateau. The AUV tows a 32-hydrophone array and a source emitting signals at regular intervals along a 14-km survey track in two frequency bands (900-1300 and 1900-3600 Hz). The reflection data are processed in terms of reflection coefficients which results in ~1600 data sets, each with a seabed footprint of <20 m. For efficient uncertainty quantification, a particle filter is applied. The inversion provides rich seabed information with resolution and geoacoustic parameter estimates significantly better than possible with vertical profiling. The survey reveals a low-velocity (<1500 m/s) wedge with low attenuation of initially 1.2-m thickness, thinning towards the Sicilian coast and disappearing after 8 km. An erosional, high-velocity boundary is increasingly buried by low-velocity material towards the coast. This boundary is rougher in shallower water and depressions are filled with material of lower velocity. [Data: CLUTTER JRP, a collaboration of ARL-PSU, DRDC, CMRE, and NRL. Research supported by SERDP and ONR ocean acoustics.]
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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 | 0.000 | 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".