Geoacoustic model for the New Jersey Shelf by inverting airgun data
Bibliographic record
Abstract
This paper describes geoacoustic inversion of airgun data acquired during the SWARM95 experiment. Hybrid optimization and Bayesian inversion techniques were applied to three airgun data sets recorded by a vertical line array. Optimization results are used to show the consistency of the estimates from all of the shots in terms of histograms and standard deviations of the inverted geoacoustic model parameters. The inversion results from the Bayesian approach are used to show the uncertainties of the estimates in terms of marginal distributions, MAP estimates, and credibility intervals. In the Bayesian inversion, full data error covariance matrices were estimated by ensemble averaging the covariance of the residuals of the measured and modeled data of inversions from many shots. The numbers of shots in the ensemble averages were determined by checking the temporal coherence of the signal. Statistical tests were used to test the validity of the assumptions in the Bayesian approach after incorporating full data error covariance matrices. With these inversion techniques, equivalent geoacoustic models with/without shear wave estimates are extracted for this experimental site. The frequency dependence of the p-wave attenuation, and the correlation between the geoacoustic parameters are obtained from the inversion results. [Work supported by ONR.]
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".