Benchmarking geoacoustic inversion methods for range-dependent waveguides
Bibliographic record
Abstract
Inversion methods have been developed over the past decade to extract information about unknown ocean-bottom environments from acoustic field data. This paper summarizes results from the Office of Naval Research/Space and Naval Warfare Systems Command (SPAWAR) Geoacoustic Inversion Techniques Workshop, which was designed to benchmark present-day inversion methods. The format of the workshop was a blind test to estimate unknown geoacoustic profiles by inversion of synthetic acoustic field data. The fields were calculated using a high-angle parabolic approximation and verified using coupled normal modes for three range-dependent shallow-water test cases: a monotonic slope; a shelf break; and a fault intrusion in the sediment. Geoacoustic profiles were generated to simulate sand, silt, and mud sediments in these environments. Several different approaches for inverting the acoustic field data were presented at the workshop: model-based matched-field methods; perturbation methods; methods using transmission loss data; and methods using horizontal array information. An effective inversion must provide both an estimate of the bottom parameters and a measure of the uncertainty of the estimated values. New methods were presented at the workshop to formalize the measure of uncertainty in the inversion. Comparisons between the different inversions are discussed in terms of a metric-based transmission loss calculated using the inverted profiles. The results demonstrate the effectiveness of present-day inversion techniques and indicate the limits of their capabilities for range-dependent waveguides.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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".