SPECTRA: A Portal to Combine and Harness the Best Satellite Methane Emissions Data Available to Guide Operators on Their Decarbonization Journey
Bibliographic record
Abstract
Abstract GHGSat continues to be the only entity in the world operating satellites dedicated to high-resolution measurements of methane emissions. Every day, GHGSat makes measurements at hundreds of facilities around the world. To optimize the targeting of its satellite observations and turn its measurements into actionable insight, GHGSat developed in-house expertise in ingesting and analyzing other relevant and complementary streams of data such as public satellites and databases of information. In the Spring of 2021, GHGSat released SPECTRA, an ESRI ArcGIS-based portal to facilitate the navigation and interpretation of its high-resolution measurements and analytics layers. The purpose of this paper is to demonstrate how GHGSat proprietary data, in combination with an innovative analytical suite, can provide situational awareness on methane emissions to operators in the regions where they operate assets and provide opportunities to mitigate them. The data analytics package under SPECTRA incorporates data and algorithms capable of providing insights into the constantly evolving O&G infrastructure. By looking at all data available, from coarse resolution regional methane concentration data to high-resolution methane measurements at facilities, from flaring data to production databases, SPECTRA provides insight on possible precursors of emission events by examining trends and patterns in the data. This paper will present results of methane emissions plumes identified with GHSGat's constellation of satellites, as well as data examples gathered with other platforms such as Sentinel 5P and Sentinel 2. The compatibility and complementarity of the different satellite platforms will be discussed, as will the ability to use low-resolution enhancements to target GHGSat's high-resolution satellite observations to identify and characterize the source of the localized higher concentration. The ability to toggle various layers of information in SPECTRA will be explored.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.017 |
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".