Bayesian Geoacoustic Inversion With the Image Source Method
Bibliographic record
Abstract
This paper develops a Bayesian approach to the image source method (ISM) for efficient inversion of seabed reflection data to estimate geoacoustic parameters and uncertainties. Based on the representation of layered seafloor-reflected signals by image sources, ISM is a very efficient method which provides the local sound-speed profile (SSP) of the sediment structure. It is a two-step method: first, the image sources are detected and localized from the recorded signals, and second, from these locations, the thickness and sound speed of each sediment layer are estimated from the Snell-Descartes law of refraction. This work focuses on the definition and construction of the image sources with a distinction between real and virtual image sources which has consequences on the uncertainties of ISM. The localization of the image sources is performed within a Bayesian formulation based on sampling the posterior probability density to estimate the median SSP and uncertainties. The algorithm is tested first on synthetic data, with results in excellent agreement with Bayesian travel-time inversion but a much lower computational cost. Finally, the Bayesian ISM is applied to at-sea data measured in the Scattering And ReverberAtion from the sea Bottom (SCARAB) experiment, which took place near Elba Island off the west coast of Italy in 1998, and the resultant sediment SSP agrees well with previous results of other geoacoustic inversion methods.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".