Bayesian matched-field inversion for shear and compressional geoacoustic profiles at the New England Mud Patch
Bibliographic record
Abstract
This paper estimates depth-dependent profiles of shear- and compressional-wave geoacoustic properties for seabed sediments at the New England Mud Patch through matched-field inversion of broadband (20-2000 Hz) acoustic data recorded at a 14-element vertical line array due to a combustive sound source. Trans-dimensional Bayesian inversion is applied to sample probabilistically over the number of seabed layers and corresponding layer depths and geoacoustic properties, as well as over the order and parameters of an autoregressive error model. This approach, based on a parallel-tempering implementation of birth/death reversible-jump Markov-chain Monte Carlo sampling, combines objective, data-driven model selection and quantitative parameter/uncertainty estimation. Results indicate low shear-wave speeds (∼30 m/s) with small uncertainties over most of the upper mud layer, increasing in underlying transition and sand layers, with values in good agreement with in situ probe measurements (for the mud) and nominal values (for sand). The compressional-wave attenuation profile is well estimated but shear-wave attenuation is poorly constrained. Comparison of results for inversions both with and without shear-wave parameters and consideration of inter-parameter correlations indicate that estimates of compressional-wave parameters, including attenuation, are not substantially influenced by shear-wave effects, with the possible exception of the sand layer.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".