Deployment-invariant probability of detection characterization for aerial LiDAR methane detection
Notice bibliographique
Résumé
Accurate detection sensitivity characterization of remote methane monitoring technologies is critical for designing, implementing, and auditing effective emissions monitoring and mitigation programs. Several research groups have developed test methods based on single/double-blind controlled release protocols and regression-based data analysis techniques to create probability of detection (PoD) models for characterizing remote sensor detection sensitivities. The previously created methods and models account for some of the important factors that affect detection sensitivity, such as wind speed, and in the case of Conrad et al. flight altitude. However, these models do not account for other important factors, such as 1) light levels received by the remote sensor due to variations in terrain albedo or other factors, 2) spatial density of remote sensing measurements, or 3) variation in individual sensor performance. In this paper, we build on the work of Conrad et al. by introducing a gas concentration noise (GCN) model for Gas Mapping LiDAR aerial methane detection technology that, when combined with wind speed at the emission location, accounts for all significant sensor and environmental parameters that affect detection sensitivity for scenarios involving an isolated emission source - a source that does not spatially overlap with a methane plume originating from another source location. We incorporate the GCN model into Conrad et al.'s PoD model and apply it to several sets of controlled release data acquired across widely varying deployment and environmental conditions to develop PoD models for Bridger Photonics Inc.'s first- and second-generation (GML 1.0 and GML 2.0, respectively) Gas Mapping LiDAR sensors. Finally, we compare controlled release data acquired by GML 2.0 in different geographic regions and terrain cover types, in different wind conditions, deployed on different aircraft types, and with different flight parameters. Results show that the GML 2.0 PoD model remains valid regardless of the location or conditions under which the sensors are deployed, and the aircraft and flight parameters used for deployment. Based on PoD measurements in 12 production basins across North America, the average 90 % PoD emission rate for sites measured by GML 2.0 in 2023 was 1.27 kg/h. • Characterizing methane emission source detection probability for aerial LiDAR scans • Tested at 4 locations across North America via helicopter and fixed-wing deployment • Detection probability based on physical model fit to controlled release data • Good agreement between model and measurements at all test locations • Enables emission source detection probability estimates for scanned infrastructure.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».