Satellite-Based Ice and Iceberg Monitoring for Offshore Engineering Design and Tactical Operations
Notice bibliographique
Résumé
Abstract Characterization of the ice environment is an essential step in the probabilistic design approach of Arctic offshore structures. Uncertainty here could lead to overly conservative designs and higher than necessary cap-ex. Inclusion of an effective ice management strategy further mitigates risk and cost. Both tactical and historical knowledge of the ice environment can be achieved cost effectively using space-based surveillance or Earth Observation (EO). The mapping and monitoring of ice visited regions is a fundamental application area for EO, in particular Synthetic Aperture Radar (SAR) missions. It is an all-weather, day-and-night, geographically independent sensor. Spaceborne SAR mapping of ice has been available since the 1970s; however routine SAR monitoring was not possible until the launch of Europe's ERS-1 satellite in 1992. This event also heralded in an era of large scale archiving of radar data. In addition to chart data available through various national ice centres, there is now 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. Over the past 5–10 years, the number of radar satellites has quadrupled and technical capabilities have increased by an order of magnitude. Weekly surveillance has been replaced with daily and performance metrics are approaching 100%. Satellites are now a reliable, effective tool for a large portion of a project's life cycle - from exploration, to developing a design basis to production. Its prevalence within the industry is growing. This paper will highlight advances in satellite monitoring, new pricing policies to increase uptake, and recent experience using satellite SAR operationally in northern oil and gas projects. Background In conducting safe and cost effective operations, ice management and risk mitigation practices are integral to operations. The first element of the ice management plan is detection of ice and icebergs. This provides a basis for all subsequent ice management actions 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 SAR is well suited to map and monitor icebergs and sea ice due to its ability to provide images day or night, through cloud or fog, independent of environmental 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 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 "operations ready" in that they provide their data in Near-Real- Time (NRT) with imagery available via the internet within hours of acquisition. The next generation of SARs to 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, with particular effectiveness in northern resource development. The increasing prevalence of SAR, along with lower data costs and flexible data policies will lead to increased use.
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 ».