Evaluation of passive acoustic methods for ambient noise baseline and gas flow rate quantification at a proposed nearshore carbon capture and storage site in Australia
Bibliographic record
Abstract
Measurement, monitoring, and verification (MMV) is an integral component of carbon capture and storage (CCS) projects. Within an operational MMV equipment, hydrophone-based passive acoustic techniques are used to establish ambient noise baseline and flow rate quantification at short range, specifically to facilitate “detect-attribute-quantify” sequence of an MMV program. However, nearshore environments are acoustically complex with different soundscape components that can disproportionately dominate ambient noise levels, potentially masking acoustic signatures of bubbles used to quantify seabed gas seeps. Therefore, a robust baseline describing ambient noise variability across the range of frequencies associated with acoustic emissions of gas seeps is required, from which changes can be detected and monitored. In this context, hydrophone measurements from a proposed nearshore CCS site in Australia are analyzed to establish a temporally resolved baseline, identifying key drivers causing overall ambient noise variability. These results are compared with acoustic bubble spectrum features and flow rate estimates from a controlled in situgas release experiment to understand the likelihood of detecting bubbles and quantifying flow rate at the proposed CCS site. Despite the complexities of nearshore environment, the evaluation highlights that passive acoustic methods can provide a practical solution to complement quantification component of operational MMV programs.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".