Bayesian Inversion of Interface-Wave Dispersion for Seabed Shear-Wave Speed Profiles
Bibliographic record
Abstract
This paper applies Bayesian inversion to estimate seabed shear-wave speed profiles and their uncertainties from interface-wave dispersion data. A nonlinear formulation is developed to estimate the most probable profile together with marginal probability distributions and credibility intervals from the posterior probability density (PPD) using adaptive hybrid optimization and Metropolis-Hastings sampling (MHS). To address correlated data errors, a full error covariance matrix is estimated from residual analysis, and rigorous a posteriori statistical tests are applied to validate the covariance estimate and the assumption of a multivariate Gaussian error distribution. The most appropriate parameterization for the shear-wave speed profile is determined using the Bayesian information criterion (BIC), which provides the simplest model consistent with the resolving power of the data. Parameterizations considered vary in the number and type of layers, and include layers with uniform speed, and with linear and power-law shear-speed gradients. For the data considered here, a power-law parameterization is indicated, which is consistent with theoretical expectations for uniform, unconsolidated sediments under overburden pressure. The maximum depth to which the dispersion data constrain the shear-speed profile is investigated using an approximate analytic formula for power-law profiles and repeated inversions in which the maximum depth to an underlying half-space is systematically increased.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".