State of the Art in Satellite Surveillance of Icebergs and Sea Ice
Bibliographic record
Abstract
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.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".