Parameter Estimate Biases in Geoacoustic Inversion From Neglected Range Dependence
Bibliographic record
Abstract
This paper shows that neglecting environmental range dependence in matched-field inversion (MFI) results in biased theory errors that lead to biased geoacoustic parameter estimates using standard inversion methods. Two approaches are used to investigate this issue. The first considers the distribution of optimal parameter estimates obtained from a large number of range-independent inversions of synthetic data generated for random range-dependent environments. The second applies Bayesian inversion and computes marginal uncertainty distributions for geoacoustic parameters, neglecting environmental range dependence. Both hard- and soft-bottom environments are considered at a number of scales of lateral variability for water depth and seabed sound speed. While the use of multifrequency data reduces the variability of the parameter estimates, it does not generally reduce parameter biases and increases biases in some cases. The biases appear to result from additional losses in range-dependent propagation, which are compensated for in range-independent inversion by adjusting geoacoustic parameters to decrease the seabed reflection coefficient. The effects of range dependence differ for different environments, with the soft-bottom case sensitive to range-dependent sound speed and the hard-bottom case sensitive to range-dependent water depth.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".