Trans-dimensional geoacoustic inversion on a range-dependent track: Using chirp subbottom survey data as prior information for seabed layering
Bibliographic record
Abstract
Geoacoustic inversion methods can be broadly divided into two categories. Fixed-dimensional methods are based on the assumption of a known environmental parametrization, including the number of seabed layers. Trans-dimensional (trans-D) methods estimate the environmental parametrization as part of the inverse problem. Although trans-D methods are very powerful, they have barely been applied in range-dependent geoacoustic inversion, and fully range-dependent trans-D inversion is a challenging problem. To mitigate this issue, we propose to use chirp subbottom survey data as prior information within a trans-D inversion method. To do so, the method considers the seabed as an unknown number of homogeneous sediment layers; the number of layers and geoacoustic properties within layers are constant over range, but layer interface depths are range dependent, with the lateral variation informed by the two-way travel-times to reflectors from the chirp survey. The method was successfully applied for single hydrophone geoacoustic inversion using data collected during the 2017 Seabed Characterization Experiment. [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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".