State of the Art in Satellite Surveillance of Icebergs and Sea Ice
Notice bibliographique
Résumé
Abstract The characterization of the ice environment is a necessary step in the probabilistic design approach of Arctic offshore structures. Without such knowledge, design uncertainty is high with the result being overly conservative designs with higher build costs to deal with the uncertainty associated with sea ice and iceberg loads. In addition to knowledge of the ice environment, the addition of ice management to operations leads to a lower risk of ice impact. When ice management is considered at the design stage, additional design concepts may be considered, which may also lead to lower build costs. A critical component to an effective ice management plan is tactical knowledge of the ice environment. Both tactical and historical knowledge of the ice environment can be achieved cost effectively using satellite monitoring. This paper examines the evolution of satellite SAR-based monitoring of sea ice and icebergs to support Arctic offshore operations, particularly for the oil and gas industry. The presentation will demonstrate, at a high level, how these data might be used by the industry, and how recent advances in satellite mapping technology add value to these services. Background In conducting safe and cost effective operations, ice management and risk mitigation practices are integral to operations. The key and primary element of the ice management plan is the detection and subsequent mapping of ice and iceberg locations, since this provides a fundamental basis for all subsequent ice management decision making such as towing and suspension of operations. Comprehensive explanations of the ice management process and technologies that can be used to facilitate an ice management plan were detailed by Randell et al. (2009). Satellite Synthetic Aperture Radar (SAR) is naturally applicable to map and monitor icebergs and sea ice due to its ability to provide images day or night, through cloud or fog, and various wind conditions. Satellite SAR mapping of ice has been available since the 1970s, although routine SAR monitoring of ice was only made possible in the 1990s with the launch of the European satellite ERS-1 in 1992. This satellite also heralded in an era of large scale data archiving of radar data. In addition to data available through various national ice centres, there is now available an archive of almost 20 years of raw satellite radar data that can be used to create highly detailed historical maps of ice and icebergs to aid in the design process. Many existing and almost all of the new SAR satellites are ‘operational’ in that they provide their data in a near-real-time (NRT) mode, with imagery available via the internet within hours of acquisition. The latest generation of SARs that will be launched within the next few years are specifying imagery delivery times of less than one hour; an investment in a ground station facility can allow data provision in minutes of acquisition. With these capabilities, SAR can be used effectively by the industry to aid in Arctic resource development. The increasing prevalence of SAR, along with lower data costs and more flexible data policies will lead to increased use by the industry into the future.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».