Extrapolation of geoacoustic properties using seismic reflection data: A shallow water example
Bibliographic record
Abstract
In areas of the world that are bottom limited, sonar performance models require accurate seabed geoacoustic properties. Geoacoustic properties, e.g., sound speed, density, and attenuation as a function of depth, frequency and geographic position, are expensive to acquire over large areas. Most geoacoustic techniques, including direct sampling (e.g., cores, probes) or acoustic inversion methods (e.g., matched field methods) spatially sample either in 1-D or 2-D. Conducting these measurements in a dense grid over large areas is not feasible and as a consequence seabed database developers rely on seismic reflection data coupled with geologic models to spatially interpolate and extrapolate the geoacoustic properties. Seismic reflection data is attractive for this purpose because it has much greater geographic coverage and contains information on underlying geologic processes that ultimately control the geoacoustic properties. The inherent strengths and weaknesses of seismic data for geoacoustic extrapolation are explored using seismic reflection transects in the Straits of Sicily together with ground-truth geoacoustic properties and their uncertainties (from wide-angle reflection measurements) at each end of the transect. The uncertainties of the extrapolated geoacoustic properties are determined with and without the ground-truth data. [Work supported by the Office of Naval Research OA321 and the NATO Undersea Research Centre.]
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".