Estimation of sea floor properties using acoustic vector sensors
Bibliographic record
Abstract
Information in the acoustic vector field can be used to estimate properties of the environment with which the field interacts. A study was performed to understand the value of vector field data when inverting for sea floor geoacoustic properties. The study compared results obtained with new inverse methods based on measurements of complex acoustic transfer functions and specific acoustic impedance. Acoustic field data consisted of gated continuous wave transmissions acquired with four acoustic vector sensors that spanned the water-sediment interface during the Sediment Acoustics Experiment 2004 (SAX04). Motion data provided by the buried vector sensors were affected by a suspension response that was sensitive to the sediment density and shear wave speed. The suspension response for the buried vector sensors included a resonance within the analysis band of 0.6–2.4 kHz. The response was sufficiently sensitive to the local geoacoustic properties, that it was exploited by the inverse methods developed for this study. Inversions of real and synthetic data sets indicated that information about sediment shear wave speed was carried by the suspension response of the buried sensors, as opposed to being contained inherently within the acoustic vector field. [Work supported by ONR.]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".