Acoustic imaging of natural gas seepage in the North Sea: Sensing bubbles controlled by variable currents
Bibliographic record
Abstract
Natural seepage from the seafloor is a worldwide phenomenon but quantitative measurements of gas release are rare, and the entire range of the dynamics of gas release in space, time, and strength remains unclear so far. To mitigate this, the hydroacoustic device GasQuant (180 kHz, multibeam) was developed to monitor the tempo‐spatial variability of gas bubble releases from the seafloor. GasQuant was deployed in 2005 on the seafloor of the seep field Tommeliten (North Sea) for 36 h. This in situ approach provides much better spatial and temporal resolution of seeps than using conventional ship‐born echo sounders. A total of 52 gas vents have been detected. Detailed time series analysis revealed a wide range of gas release patterns ranging from very short periodic up to 50 min long‐lasting events. The bulk gas seepage in the studied area is active for more than 70% of observation time. The venting clearly exhibits tidal control showing a peak in the second quarter of the tidal pressure cycle, where pressure drops fastest. The hydroacoustic results are compared with video observations and bubble flux estimates from remotely operated vehicle dives described in the literature. An advanced approach for identifying and visualizing rising bubbles in the sea by hydroacoustics is presented in which water current data were considered. Realizing that bubbles are moved by currents helps to improve the detection of gas bubbles in the data, better discriminate bubbles against fish echoes, and to enhance the S/N ratio in the per se noisy acoustic data.
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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 | 0.000 |
| 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 | 0.000 |
| 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".