Data error estimation for matched-field geoacoustic inversion
Bibliographic record
Abstract
Nonlinear Bayesian methods have been applied to geoacoustic inversion to estimate uncertainties for seabed parameters by sampling the posterior probability density. This procedure requires quantifying the errors on the acoustic data, including both measurement and theory errors, which are generally not well known. To date, point estimates for data errors have been derived using a global maximum likelihood approach. However, this is not consistent with the Bayesian formulation, and ignores the effects of uncertainty in the error estimates and interdependencies between the data errors and geoacoustic parameters. The Bayesian approach treats the data errors as random variables and includes them as additional parameters within the inversion. However, this increases significantly the number of unknowns and the computational effort. A third approach is to use a local maximum likelihood error estimate evaluated independently for each geoacoustic model considered in the sampling procedure. This has the benefit of not increasing the number of unknowns or computational effort, but includes some of the effects of the error uncertainties and interdependencies. The three approaches are compared for Bayesian matched-field geoacoustic inversion of both synthetic and experimental data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".