BENCHMARKING RANGE-DEPENDENT PROPAGATION MODELING IN MATCHED-FIELD INVERSION
Bibliographic record
Abstract
This paper considers how the accuracy of range-dependent propagation modeling affects the results of matched-field inversion (MFI) for seabed geoacoustic parameters. In MFI, the forward problem of computing the acoustic fields associated with candidate geoacoustic models is solved a large number of times. Given significant mismatch due to measurement and theory errors, together with the computationally intensive nature of MFI, the appropriate tradeoff between modeling accuracy and computational speed is not obvious. This tradeoff is considered here in terms of the degradation in information content for the geoacoustic parameters that results from inaccurate propagation modeling. The information content is quantified using the marginal posterior probability distributions of the geoacoustic parameters, as computed from a fast Gibbs sampling approach to Bayesian inversion. A synthetic example of this analysis is presented in which the parabolic equation is used to model acoustic fields for a shallow-water, upslope environment, with different levels of modeling accuracy/speed controlled by the range and depth step sizes of the computational grid.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".