Comparison and combination of matched-field and modal-dispersion inversion for seabed geoacoustic profiles at the New England Mud Patch
Bibliographic record
Abstract
This paper considers the information content for seabed geoacoustic inversion of recorded acoustic waveforms processed as modal-dispersion (MD) data (mode arrival times as a function of frequency) and matched-field (MF) data (multifrequency complex acoustic fields across a sensor array). These approaches are applied separately and combined in joint inversion, where the MD and MF data sets are derived from the same acoustic recordings collected during the 2017 Seabed Characterization Experiment on the New England Mud Patch. Unlike MD inversion, MF inversion requires knowledge of source and receiver depths, and the complex source spectrum must be estimated as part of the inversion. However, MF inversion is sensitive to seabed attenuation (MD is not) and more easily extended to higher frequencies, where mode filtering for MD data is challenging. Comparison of geoacoustic information content is facilitated here using trans-dimensional Bayesian inversion to sample probabilistically over the number of layers of the seabed model as well as the order of an autoregressive error model. Results indicate MF inversion resolves more detailed geoacoustic structure with smaller uncertainties, including good estimates of the attenuation profile for wideband (20-1504 Hz) inversions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".