Bayesian geoacoustic inversion of time-averaged horizontal-array data
Bibliographic record
Abstract
This paper considers quantifying data errors in Bayesian geoacoustic inversion applied to time-averaged data. Cross-spectral density matrices are formed by averaging spectra from a sequence of time-series segments (data snapshots). Error estimation for snapshot-averaged data has typically assumed either that averaging reduces errors as if they are fully independent between snapshots (an optimistic assumption), or that averaging does not reduce errors at all (a pessimistic assumption). Data errors are quantified here assuming that averaging reduces measurement error (dominated by ambient noise, which can be reasonably assumed independent), but does not reduce theory (modeling) error. This provides a physically reasonable intermediate result between the optimistic and pessimistic assumptions. Bayesian inversion is applied to data collected by FFI with a bottom-mounted horizontal array at a shallow-water site in the Barents Sea. Supporting geophysical measurements (seismic reflection and refraction, bottom-penetrating sonar, gravity core) provide independent information on seabed properties. A towed acoustic source transmitted multiple low-frequency tones at levels comparable to those of a merchant ship. Inversion results in the form of marginal posterior probability distributions are compared for the different approaches to data error estimation, and for data collected for several source ranges and bearings.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".