Geoacoustic inversion of the pressure gradient data using a single vector sensor
Bibliographic record
Abstract
A vector sensor has the capability of obtaining the acoustic field involving the sound pressure and three-dimensional particle velocities simultaneously, which means that there exists a potential to extract deep information for further applications like direction of arrival (DOA), positioning, classification, geoacoustic inversion, etc. This paper applies a nonlinear Bayesian approach to the pressure gradient data for geoacoustic properties of South China Sea, about which the experiment took place in July of 2013 in South China Sea of nearly 90 m depth. Recorded linear frequency modulation (LFM) signals of the pressure and vertical particle velocity are windowed and processed together to estimate the pressure gradient in the vertical direction at multiple frequencies as the observation data. After the optimal sediment model is selected based on BIC (Bayesian Information Criterion) using adaptive simplex simulated annealing (ASSA), delayed rejection adaptive Metropolis (DRAM), a Markov chain Monte Carlo (MCMC) sampling method, is used not only to provide a maximum a-posteriori (MAP) parameters estimates but also to quantify the parameters uncertainties and inter-parameter relationships. [Work supported by the National Natural Science Foundation (Grant No. 61531012).]
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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 | 0.001 |
| 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".