Synthetic Modeling for an Acoustic Exploration System for Physical Oceanography
Bibliographic record
Abstract
Abstract Marine multichannel seismic (MCS) data, used to obtain structural reflection images of the earth’s subsurface, can also be used in physical oceanography exploration. This method provides vertical and lateral resolutions of O (10–100) m, covering the existing observational gap in oceanic exploration. All MCS data used so far in physical oceanography studies have been acquired using conventional seismic instrumentation originally designed for geological exploration. This work presents the proof of concept of an alternative MCS system that is better adapted to physical oceanography and has two goals: 1) to have an environmentally low-impact acoustic source to minimize any potential disturbance to marine life and 2) to be light and portable, thus being installed on midsize oceanographic vessels. The synthetic experiments simulate the main variables of the source, shooting, and streamer involved in the MCS technique. The proposed system utilizes a 5-s-long exponential chirp source of 208 dB relative to 1 μ Pa at 1 m with a frequency content of 20–100 Hz and a relatively short 500-m-long streamer with 100 channels. This study exemplifies through numerical simulations that the 5-s-long chirp source can reduce the peak of the pressure signal by 26 dB with respect to equivalent air gun–based sources by spreading the energy in time, greatly reducing the impact to marine life. Additionally, the proposed system could be transported and installed in midsize oceanographic vessels, opening new horizons in acoustic oceanography research.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".