Reflection-coefficient inversion for compressional- and shear-wave dispersion in porous and elastic layered media
Bibliographic record
Abstract
This paper considers Bayesian inversion of seabed reflection-coefficient data for the compressional- and shear-wave velocity dispersion and attenuation-frequency dependence in arbitrarily layered porous and elastic media. The seabed is modeled using Buckingham's viscous grain shearing model which obeys causality. Seabed layers are parametrized in terms of six fundamental parameters, including thickness, porosity, compressional and shear grain-to-grain moduli, material exponent, and the compressional visco-elastic time constant. These fundamental parameters are used to compute density, compressional- and shear-wave dispersion curves, and compressional and shear attenuation-frequency curves for each layer. The curves are used in the inversion to predict spherical-wave reflection coefficients as a function of frequency (300–3000 Hz) and grazing angle (12–75 degrees), which include the effects of shear waves in arbitrarily layered media. In addition, the seabed layering is estimated from the data by applying a trans-dimensional Bayesian model. The ability to resolve shear-wave velocity and attenuation structure is studied using simulated data. Finally, compressional- and shear-wave dispersion are presented and discussed from measured reflection data at a sandy site in the Tyrrhenian Sea. [Work supported by ONR Ocean Acoustics.]
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".