Hybrid geoacoustic inversion of broadband Mediterranean Sea data
Bibliographic record
Abstract
This paper describes an acoustic experiment (PROSIM'97) carried out to investigate inversion for seabed properties at a site off the west coast of Italy where previous acoustic and geophysical studies have been performed. Acoustic fields were measured at a vertical hydrophone array due to a swept-frequency source towed over weakly range-dependent bathymetry. Based on the known geology, the seabed is modeled as a sediment layer overlying a semi-infinite basement with unknown model parameters consisting of the sediment thickness, sediment and basement sound speeds, source range and depth, water depth at the source and array, and array tilt. A hybrid inversion algorithm is applied to determine the model values that minimize the mismatch with the measured acoustic fields. Multiple data sets are analyzed to examine the consistency of the inversion results. It is found that the low sound speed of the sediment layer, together with a large uncertainty in bathymetry, leads to strong correlations between the water depths and sediment thickness. This precludes reliable estimation of these parameters individually; however, the total depth to the basement can be estimated reliably. In addition, the basement speed and geometric parameters are estimated consistently, and all parameters compare favorably with the geophysical ground-truth information and with previous inversion results.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".