Bayesian geoacoustic inversion of 1-2 kHz seabed reflection data for layered muddy sediments
Bibliographic record
Abstract
This paper presents trans-dimensional Bayesian geoacoustic inversion of seabed reflection data for sub-bottom geoacoustic profiles and associated uncertainty estimation at the New England Mud Patch. The data considered here are wide-angle seabed reflection coefficients as a function of grazing angle and frequency measured during the 2017 Seabed Characterization Experiment. Chirp pulses over a frequency band of 1–6 kHz were transmitted by an omnidirectional acoustic source towed by a research vessel at a speed of about 4 knots and recorded at a bottom-moored hydrophone. High signal-to-noise-ratio reflection coefficients from 1–2 kHz and angular coverage of ∼15–25° are considered here for geoacoustic inversion. This frequency range is higher than for previous reflection-coefficient data sets on the Mud Patch. The angular range, although relatively narrow, includes strong Bragg resonances which provide information on the sediment layering properties. The inversion applies the viscous grain-shearing sediment acoustics model, which provides dispersive (frequency-dependent) results for sound speeds and attenuations. The seabed structure estimated here is compared to previous inversion results and to core measurements in the vicinity. [Work supported by the Office of Naval Research.]
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".