Efficient geoacoustic inversion of spherical-wave reflection coefficients for muddy seabeds
Bibliographic record
Abstract
This paper presents efficient geoacoustic inversion of high-resolution reflectivity data for a seabed site consisting of a thick mud layer (~10 m) over multi-layered sandy sediments. Wide-angle, broadband reflection-coefficient data were collected using a towed source and a recording hydrophone located a few meters above the seabed. Trans-dimensional Bayesian inversion is applied to estimate sound-speed, density, and attenuation profiles for an unknown number of sediment layers. Due to the experiment geometry, the data should be modeled as spherical-wave reflection coefficients, which require numerical integration of rapidly oscillating functions and can be computationally intensive. Here, a speed-up of two to three orders of magnitude is achieved by introducing a fast Levin-type numerical integration implemented on a graphics processing unit. The new fast integration algorithm/implementation alleviates time constraints for the spherical-wave inversion, which would otherwise require weeks of computation time, and precludes the use of fast but less-accurate plane-wave theory. Inversion results are presented for simulated data and for experimental data collected on the western Malta Plateau. The analysis of muddy sediments in this work is expected to provide insight into the geoacoustics of the New England mud patch, location of the upcoming ONR Seabed Characterization Experiment 2016 (SCAE16).
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".