Bayesian inversion of frequency-averaged reflection data
Bibliographic record
Abstract
This paper presents a nonlinear Bayesian inversion of high-resolution seabed reflection data to estimate visco-elastic parameters of the upper sediments. The inversion is applied to data from two sites in the Strait of Sicily. One site is characterized by low-velocity, silty-clay sediments, resulting in data with a well-defined angle of intromission. The second site is characterized by high-velocity clayey sand, resulting in a critical angle. The data were frequency averaged from 500–2000 Hz and inverted for visco-elastic parameters of a half-space seabed model. The likelihood function employed in the inversion is based on the assumption of independent, Gaussian-distributed data errors, with the standard deviation included as a nuisance parameter in the inversion. Statistical tests are applied to the data residuals a posteriori to validate these assumptions. Good results are obtained for sediment compressional-wave velocity, compressional attenuation, and density; shear parameters are less-well determined although low shear-wave velocities are indicated. The Bayesian analysis provides a quantitative comparison of inversion results for the two sites in terms of the resolution of specific geoacoustic parameters, and indicates that the geoacoustic information content is significantly higher for angle-of-intromission data.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".