UAV-based measurement of natural gas seeps using a newly developed ultra-lightweight high-sensitivity methane sensor in the western Canadian Arctic
Bibliographic record
Abstract
Airborne eddy-covariance measurements over the outer Mackenzie River delta in the western Canadian Arctic have linked significant methane (CH4) emissions to geological sources from subsurface reservoirs. However, few natural gas seeps have ever been mapped. Efforts by airborne imaging spectroscopy to locate these methane ‘hotspots’ primarily attributed higher emissions to biogenic CH4 from wetlands as a function of the water table. Resolving the discrepancies between these findings requires identifying seeps within areas of high background emissions. Conducting ground-based measurement surveys to achieve this is challenging in wetlands due to the impedance of widespread bodies of water and the risk of releasing CH4 when disturbing the soil during on-foot surveys.To aid in identifying methane seeps that have not been mapped before, we present an ultra-lightweight in-situ methane sensor, and its deployment on a common commercially available Uncrewed Aerial Vehicle (UAV) – a DJI Matrice 300 RTK. This system was tested in a location in the Mackenzie River delta where CH4 is known to seep to the surface through conduits in thin, thawing permafrost overlying underground hydrocarbon reservoirs. The easily transportable UAV permits non-invasive, near-surface flight capabilities with highly flexible flight plans, while the sensor’s lightweight and power-efficient design permits high sensitivity for detecting and quantifying subtle variations in atmospheric CH4 concentrations, even in remote and challenging environments.Our miniaturized, mid-infrared tunable diode laser absorption spectroscopy CH4 sensor targets CH4’s strongest rotational-vibrational transition at the 3270 nm wavelength. Employing the wavelength modulation technique and a small open-path gas absorption cell, the sensor is able to resolve atmospheric CH4 concentrations as low as 10 ppb (parts per billion) with a near-instantaneous response time (100 Hz sample rate) making it suitable for deployment on fast moving aerial platforms. The entire standalone instrument package weighs 1.2 kg and is ideal for integration on consumer UAVs which have limited payload capacities.We flew the UAV in horizontal grid patterns typically used in source detection and localization scenarios, as well as vertical “curtain” patterns to sample cross sections of the CH4 plume arising from a known gas seep to quantify the flux rate. Preliminary data analysis using a Gaussian plume inversion technique yields a CH4 emission flux estimate near 8 kg hr-1, which is comparable to fugitive emissions from some oil and gas production facilities in Canada. Our results emphasize the significance of this approach to reliably, effectively, and precisely quantify CH4 emission from natural sources, as it will enable us to identify sources of CH4 hotspots and test our hypothesis that the magnitude and frequency of these emissions will increase throughout the study region as the climate warms.
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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 | 0.001 | 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".