Trans-dimensional Bayesian Inversion for Seabed and Water-column Models
Bibliographic record
Abstract
Geoacoustic inversion requires specification of the depthdependent parameterization for the seabed model parameters.In cases where the water-column sound-speed profile (SSP) is of special interest or not sufficiently well-known, the SSP can also be parameterized and included in the inversion.For quantitative inversions, these parameterizations (seabed and water column) must be consistent with the resolving power (information content) of the acoustic data to be inverted.Trans-dimensional (trans-D) Bayesian inversion represents an automated approach to quantitative model selection, based on sampling probabilistically over various choices of parameterization.Here trans-D inversion is applied separately to seabed and water-column models.The trans-D seabed model is formulated as an unknown number of uniform layers, while the SSP is formulated as an unknown number of depth/sound-speed nodes.The Bayesian formulation allows different levels of prior information to be applied to the seabed and water column to represent different problems of interest; for example, either the seabed or the water column (or both) could be the primary goal of inversion.The joint trans-D inversion approach is illustrated here for the inversion of modal-dispersion data, considering data collected on the New England Mud Patch.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".