From geoacoustic inversion to seabed tomography using a distributed network of sources and receivers
Bibliographic record
Abstract
Understanding and predicting acoustic propagation at sea is critical for many marine applications, from noise pollution forecasts to underwater warfare. To do so in coastal waters requires knowledge of the seabed geoacoustic properties; estimating those from ocean acoustic data is called geoacoustic inversion. Historically, geoacoustic inversion considers acoustic propagation between a fixed (or linearly moving) source and a receiver (or array of receivers), usually leading to the estimation of a depth-dependent geoacoustic profile, assumed to be representative of the propagation track. In this talk, we will show how low-cost instrumentation and advanced signal processing methods (warping time-frequency analysis, trans-dimensional inversion, and machine learning) enable estimation of the spatial variability of seabed geoacoustic properties. Several examples will be presented, all based on data collected on the New England Mud Patches during the Seabed Characterization Experiments. We will notably illustrate how the proposed methods enable characterization of the spatial variability of the muddy seabed sediments at the scale of a single mud patch, as well as the inter-comparison of mud properties between several distant mud patches. [Work supported by the Office of Naval Research.]
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".