Regularized matched-mode localization with environmental mismatch
Bibliographic record
Abstract
This paper considers a new approach to matched-mode processing (MMP) for source localization. The MMP consists of decomposing far-field acoustic data to obtain the modal excitations, then matching these with modeled replica excitations. A potential advantage of MMP over matched-field processing (MFP) is that subsets of the complete mode set can be considered. For example, if geoacoustic properties are poorly known, the matching can be applied only to low-order modes that interact minimally with the seabed. However, modal decomposition can be ill posed and unstable if the sensor array does not adequately sample the acoustic field. For such cases, standard decomposition methods yield minimum-norm solutions that are biased towards zero. Although these methods provide mathematical solutions (stable solutions that fit the data), they may not represent physically meaningful solutions. The new approach of regularized MMP (RMMP) carries out an independent decomposition prior to comparison with the replica excitations for each grid point, using the replica itself as the prior estimate in a regularized inversion. This provides a more meaningful decomposition near the actual source location. In this paper, RMMP, MMP, and MFP are compared for realistic test cases, including various sensor array configurations, as well as environmental mismatch in seabed properties.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".