Bayesian layer-stripping inversion of seabed reflection data
Bibliographic record
Abstract
This paper develops a Bayesian inversion technique for recovering multilayer geoacoustic profiles using seabed reflection data. The measured data originate from acoustic time series windowed for a single bottom interaction, which are processed to yield spherical reflection coefficients (i.e., a response function of frequency and angle analogous to plane-wave reflection coefficients). Replica data are computed using a wave number-integration model (OASES) to calculate the full complex acoustic pressure field, which is processed to produce a similar seabed response function. The inversion results are compared to those obtained using plane-wave reflection coefficients. To address the high computational modeling costs, the Bayesian algorithm is implemented for a massively parallel computer. Further, the data are time windowed and divided into several layer packets, wherein each packet contains the seabed response to a certain depth. This layer-stripping approach uses the results of the previous layer packet as prior information for subsequent packets. The resulting posterior probability density for the final packet is considered the full solution to the inverse problem, and is interpreted in terms of optimal parameter estimates, marginal distributions, credibility intervals, and parameter correlations.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".