Array element localization for towed marine seismic arrays
Bibliographic record
Abstract
This paper presents an approach to array element localization (AEL) for towed marine seismic arrays based on regularized inversion of direct and bottom-reflected acoustic ray travel times picked from recorded seismic sections. Depth-sensor measurements at a number of points along the array are included as a priori estimates (with uncertainties) in the inversion. The smoothest array shape consistent with the acoustic data and prior estimates is determined by minimizing the array curvature or roughness. A smooth array shape is physically reasonable; in addition, minimizing curvature provides a priori information about the correlation between hydrophone positions that allows the estimation of both the offset and depth of hydrophones that record only one (or even no) acoustic arrival due to the shadowing effects of water-column refraction or reflection from arbitrary bathymetry. The AEL inversion is applied to a 102-sensor, 1.2-km towed array to correct receiver positions in the seismic velocity analysis of a seabed gas hydrate survey.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".