Ice-sheet velocity mapping: a combined interferometric and speckle-tracking approach
Bibliographic record
Abstract
Abstract The first and second RADARSAT Antarctic Mapping Missions (AMM-1 and -2) have now acquired interferometric synthetic aperture radar (SAR) overmuch of the ice sheet. The RADARSAT 24 day repeat cycle is nearly ideal for measuring slow ice motion (e.g. <100ma–1), but application of SAR interferometry is limited in faster-moving areas. With a 1day repeat period, ERS-1/-2 tandem SAR data are much better matched to fast motion, but are not always available. Fortunately, several authors have demonstrated the ability to measure velocity in fast-moving areas by tracking SAR speckle from image to image, which works well even in the absence of visible features. While these estimates have intrinsically lower resolution and poorer accuracy than direct phase measurements, they serve well in areas where there are no data suitable for conventional interferometry. This paper describes algorithms I have developed for merging interferometric and speckle-tracking data from multiple swaths to form a single seamless mosaic of velocity. At each point in the mosaic, all the available data are combined to produce estimates of the velocity and the associated error. This technique is demonstrated using RADARSAT data collected over Lambert Glacier, Antarctica, during AMM-1 and -2.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".