INVERSION OF GEOACOUSTIC MODEL PARAMETERS USING SHIP NOISE
Bibliographic record
Abstract
Estimation of geoacoustic models of the sea bed is an underlying research issue in understanding acoustic propagation in shallow water environments where the propagation is generally bottom limited. Inversion methods based on matched field processing have become widely used in applications with experimental data at various sites worldwide. Traditionally, the experiments have been carried out with controlled source geometries. This paper presents a new experimental approach that makes use of the noise radiated by passing ships as the sound source for the inversion. Ship noise data were measured on a 16-element vertical line array in shallow water off the west coast of Vancouver Island. The data were filtered into low (70-110 Hz) and high (170-290 Hz) frequency bands, and processed in an inversion algorithm based on back propagation of the spectral components of the noise signal. The geoacoustic model that generated the most accurate focus at the source location was taken as the best estimate. The band limited data allowed estimation separately of geoacoustic model parameters of the sea floor with the high frequencies, and then for the deeper layers using the low frequencies. The estimated model parameters compared well with ground truth data from a seismic survey and from sediment samples at the site
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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) | 0.001 | 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 | 0.000 |
| 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".