Bibliographic record
Abstract
Inversion methods for estimating geoacoustic model parameters from acoustic field data have been applied with considerable success over the past decade. The most effective methods that have been developed generally fall into two categories, nonlinear methods that are posed as optimization problems, and linearized methods that invert quantities such as horizontal wave numbers that are derived from the acoustic field data. For either case, the complete solution of the geoacoustic inverse problem requires a measure of the error for the estimated parameter, as well as the estimate itself. This paper describes an approach for specifying an error measure in nonlinear inversion processes based on matched field processing. The inversion uses an optimization algorithm that combines global and local search processes to sample the model parameter space. An effective error measure is obtained from information in scatter plots of the cost function versus parameter values for models that were tested in the search process. Examples are presented to demonstrate the application of the method to test cases from the recent Geoacoustic Inversion Benchmark Workshop, and from experimental data from vertical line arrays and seafloor horizontal arrays. [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 | 0.019 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".