Toward the detection of oil spills in sea ice-covered waters using C-band radar remote sensing
Bibliographic record
Abstract
The Arctic ice-covered waters are becoming more vulnerable to oil spills due to climate-driven sea ice loss, which has increased vessel traffic and natural resource extraction. In preparation for future incidents, this thesis presents the research undertaken in the area of Arctic crude oil spill response, with the ultimate goal of accurately detecting and characterizing such spills in sea ice-covered waters using C-band radar remote sensing. The research focuses on three interconnected objectives: observation, discrimination, and modeling of surface-based C-band scatterometer data collected during experiments involving spilled crude oil in newly formed sea ice (NI) at the University of Manitoba’s Sea-ice Environmental Research Facility. The observational study seeks to establish a definitive relationship between radar signatures and the physical properties of oil-contaminated NI. The results show that multipolarization radar signatures exhibit distinct responses when oil is encapsulated within the ice, up until the oil migrates onto the ice surface. For instance, when oil is encapsulated within the ice, a 13-dB local maximum in cross-polarization was observed with a coincidental 9-dB drop in co-polarization backscatters. The discrimination study uses radar polarimetric parameters (such as entropy, mean-alpha, copolarization correlation coefficient, conformity coefficient, and more) to accurately differentiate between uncontaminated and oil-contaminated NI. The findings reveal that a threshold classification plane of 0.3 entropy and 18° mean-alpha effectively distinguishes oil-contaminated ice. To minimize oil spill false alarms, the copolarization correlation coefficient and conformity coefficient emerge as the most reliable parameters for detecting spilled oil events in NI-covered waters. The modeling study applies the small perturbation method and particle swarm optimization in an electromagnetic inversion strategy to estimate the oil thickness on NI surface through radar simulations and observations. The simulation results indicate that radar backscatter increases with thicker oil layer, while the inversion results successfully estimated a 5-mm oil thickness on the ice surface, with an 8% overestimation. By achieving these objectives, this thesis contributes significantly to the advancement of both current and future C-band radar satellites, providing critical information, including the spill’s location, extent, and thickness, for an effective oil spill response in the remote and hostile Arctic region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".