Using a satellite-aircraft hybrid system based on the same sensor to monitor oil and gas facilities for methane emissions
Bibliographic record
Abstract
Monitoring methane emissions from oil and gas facilities requires the combination of several technologies to gain a full understanding of the challenge at a manageable cost. The integration of frequent and affordable high resolution satellite measurements to find the larger leaks with less frequent, but more expensive, aerial surveys, forms the basis of a tiered monitoring system showing great promise to optimise leak detection and repair activities. In this extended abstract, examples of methane emissions measurements from controlled releases and at oil and gas facilities acquired with both GHGSat’s second satellite, Iris (launched in September 2020) and the airborne variant of the same sensor are presented. While the combination of different technologies is not uncommon, this system is the first in the world utilising the same sensor at two different altitudes. The performance parameters of each system are highlighted and supported with recent examples. In addition, the advantages of the hybrid system will be discussed, including the opportunity for cross-validation of measurements. Finally, the potential of such a system to be used for regulatory reporting purposes will be discussed and contrasted to the standard of performing optical gas imaging camera campaigns three times a year used in some jurisdictions, notably in Canada and the United States.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".