Source bearing estimation in the Arctic Ocean using ice-mounted geophones
Bibliographic record
Abstract
This paper investigates the use of geophones mounted on the surface of Arctic sea ice for estimating the bearing to acoustic sources in the water column. The approach is based on measuring ice seismic waves for which the direction of particle motion is oriented radially outward from the source. However, the analysis is complicated by the fact that sea ice supports several types of seismic waves, producing complex particle motion that includes significant nonradial components. To suppress seismic waves with transverse particle motion, seismic polarization filters are applied in conjunction with a straightforward rotational analysis (computation of particle-motion power as a function of angle). The polarization filters require three-dimensional (3D) measurements of particle motion, and apply theoretical phase relationships between vertical and horizontal components for the various waves types. In addition, the 180 degrees ambiguity inherent in the rotational analysis can be resolved with 3D measurements by considering particle motion in the vertical-radial plane. Arctic field trials were carried out involving two components. First, a hammer source was used to selectively excite the various ice seismic waves to investigate their propagation properties and relative importance in bearing estimation. Second, impulsive acoustic sources were deployed in the water column at a variety of bearings and ranges from 200-1000 m. For frequencies up to 250 Hz, source bearings are typically estimated to within an average absolute error of approximately 100.
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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 | 0.002 |
| 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".