Quantifying the uncertainty of geoacoustic parameter estimates for the New Jersey shelf by inverting air gun data
Bibliographic record
Abstract
This paper describes geoacoustic inversion of low frequency air gun data acquired during an experiment on the New Jersey shelf. Hybrid optimization and Bayesian inversion techniques based on matched field processing were applied to multiple shots from three air gun data sets recorded by a vertical line array in a long-range shallow water geometry. For the Bayesian inversions, full data error covariance matrix was estimated from a set of consecutive shots that had high temporal coherence and small spatial variation in source position. The effect of different data error information on the geoacoustic parameter uncertainty estimates was investigated by using the full data error covariance matrix, a diagonalized version of the full error covariance, and a diagonal matrix with identical variances. The comparison demonstrated that inversion using the full data error information provided the most reliable parameter uncertainty estimates. The inversions were highly sensitive to the near sea floor geoacoustic parameters, including sediment attenuation, of a simple single-layer geoacoustic model. The estimated parameter values of the model were consistent with depth averaged values (over wavelength scales) of a high resolution geoacoustic model developed from extensive ground truth information. The interpretation of the frequency dependence of the estimated attenuation is also discussed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".