Bayesian Geoacoustic Inversion With the Image Source Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it