Transdimensional Geoacoustic Inversion Using Prior Information on Range-Dependent Seabed Layering
Bibliographic record
Abstract
This article proposes a transdimensional (trans-D) geoacoustic inversion method adapted to range-dependent (RD) propagation tracks based on prior information from a high-resolution seismic survey. Most trans-D inversions to date model the seabed as a stack of range-independent homogeneous layers, with unknown geoacoustic parameters and an unknown number of layers. The proposed method models the seabed as an unknown number of homogeneous sediment layers with an RD thickness structure and applies an adiabatic normal-mode model to predict acoustic propagation. To do so, the method extrapolates trans-D seabed models proposed at the receiver position over the range of the propagation track using reflector-interface information from a seismic survey. The method is applied successfully to modal time–frequency dispersion data collected over an RD track during the 2017 Seabed Characterization Experiment (SBCEX).
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".