A critical review of geoacoustic inversion: What does it really tell us about the ocean bottom?
Bibliographic record
Abstract
Estimation of parameters of geoacoustic models from acoustic field data has been a central theme in acoustical oceanography over the past three decades. Highly efficient numerical techniques based on Bayesian inference have been developed that provide estimates of geoacoustic model parameters and their uncertainties. However, the methods are model-based, requiring accurate knowledge of the acoustic propagation conditions in the ocean to carry out the inversion. More recent research has revealed fundamental limitations of model-based inversion methods in conditions of unknown temporal and spatial variations in the water. In addition, the inversions can generate only effective models of the true structure of the ocean bottom, which are generally highly variable over relatively small spatial scales. There are other questions about the theory for sound propagation in porous sediment media that raise doubt about the validity of inversion results. In most inversions, a visco-elastic theory is used, but is this correct? This paper reviews successes and failures of geoacoustic inversion to understand the limitation of model-based methods. Research directions are suggested in conclusion that show promise for development of new approaches. [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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".