Three-dimensional localization of transient acoustic sources using an ice-mounted geophone
Bibliographic record
Abstract
This paper presents an approach to three-dimensional (3D) localization of ocean acoustic sources using a single three-component geophone on Arctic sea ice. Source bearing is estimated by maximizing the radial signal power as a function of horizontal look angle, applying seismic polarization filters to suppress shear waves with transverse particle motion. The inherent 180° ambiguity is resolved by requiring outgoing (prograde) particle motion in the radial-vertical plane. Source range and depth estimates and uncertainties are computed by Bayesian inversion of arrival-time differences of the water-borne acoustic wave and ice seismic waves, including the horizontally-polarized shear wave and longitudinal plate wave. The 3D localization is applied to geophone recordings of impulsive sources deployed in the water column at a series of ranges (200 to 1000 m) and bearings (0° to 90°) for three sites in the Lincoln Sea characterized by smooth annual ice, rough/ridged annual ice, and thick multi-year ice. Good bearing estimates are obtained in all cases. Range-depth localization is successful for ranges over which ice seismic arrivals could be reliably detected, approximately 200 m on rough ice, 500 m on smooth ice, and 800 m on multi-year ice. Effects of environmental uncertainty on localization are quantified by marginalizing over unknown environmental parameters.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".