Benchmarking Geoacoustic Inversion Methods Using Range-Dependent Field Data
Bibliographic record
Abstract
Over the past decade, inversion methods have been developed and applied to acoustic field data to provide information about unknown ocean-bottom environments. An effective inversion must provide both an estimate of the bottom parameters and a measure of the uncertainty of the estimated values. This paper summarizes results from the Office of Naval Research (ONR)/Space and Naval Warfare Systems Command (SPAWAR) Geoacoustic Inversion Techniques Workshop, test cases 4 and 5. The workshop was held to benchmark present-day inversion methods for estimating geoacoustic profiles in shallow water. The format of the workshop was a blind test to estimate unknown geoacoustic profiles by inversion of measured acoustic transmission loss data in octave bands and reverberation envelopes. The data sets for test cases 4 and 5 were taken at two locations in shallow water, one in the East China Sea and the other along the southwest coast of Florida. The limitations of the data and the limits to the knowledge of the sites are discussed. In both cases, impulsive sources were used in conjunction with air-deployed sonobuoys. Since the measured data was incoherent, only methods consistent with total energy matching were applicable. Comparisons between the different inversion techniques presented at the workshop are discussed. For test cases 4 and 5, a precise metric was unavailable for comparison.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".