Risk Management for Autonomous Underwater Vehicles Operating Under Ice
Bibliographic record
Abstract
Abstract Autonomous underwater vehicles (AUVs) have a role to play in several phases of offshore exploration and production in the Arctic. They have the potential to enable year-round data gathering under sea ice. However, this potential can only be realized if their reliability is sufficiently high and sufficiently well established. This paper describes a Risk Management Process-AUV that has been developed to assess (a) the probability of losing an AUV and (b) the availability of an AUV, that is, " How likely is it that, when required, the AUV is ready to begin its operations??? Assessing these probabilities requires bringing together knowledge of vehicle faults and incidents, a body of knowledge on the operating environment, and how the vehicle and environment interact. The tools necessary to make these assessments are described. Examples are given of how they can be used by owners and operators to provide a clear and traceable derivation of how the risks have been estimated. These examples draw upon risk assessments for the Autosub3 AUV in Polar Regions. Introduction The scientific community, often with support from the military, has been using Autonomous Underwater Vehicles (AUVs) in the Arctic for over 40 years (ECOR, 2010 [1]). This community has demonstrated the utility of these vehicles when operating from ships or from ice camps, on fast ice or on drifting ice floes. In early 2010 the pioneering 300km round-trip mission in the Canadian Arctic by the ISE Explorer vehicle demonstrated what is now possible from remote ice camps (Kaminski et al., 2010 [2]). These through-ice hole operations during Project Cornerstone are likely to be the precursor of further AUV operations in the high Arctic. There are no longer insurmountable technical hurdles for similar tasks by Explorer or other AUVs for use in arctic waters subject to seasonal or permanent ice cover. Building on this expeditionary experience, commercially available AUVs now have the potential to contribute on an operational basis to several tasks related to offshore exploration and production in arctic seas. Several of these tasks mirror those required in temperate seas where use of AUVs for seabed survey in connection with initial site survey for platforms and pipelines is now very well established (for example, Chance, 2003 [3]). Other tasks are specific to the Arctic, such as ice monitoring and management systems for sea ice and icebergs. In these tasks AUVs could augment satellite remote sensing, ice models and ice forecasts as they have an unique ability to gather accurate data on ice draft on a spatial scale that is relevant to real-time decision support. The additional information on ice type and detailed morphology gained through multibeam imagery of the under side of ice (for example, Wadhams et al., 2006 [4]) would be a valuable contribution to an ice management decision support system. Other operations such as pipeline touch-down monitoring, routine environmental monitoring of cutting piles and produced water, and emergency response data-gathering, including quantifying dissolved hydrocarbons, deposits on the seabed, or under ice, and monitoring currents to provide information for spill dispersal models can be augmented using AUV technology.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".