Interferometric discrimination of cross-track bed clutter in ice-penetrating radar sounding data
Bibliographic record
Abstract
Abstract The interpretations of relevant interfaces (i.e. the surface and bed) in radar sounding datasets over glaciers and ice sheets are primary boundary conditions in a variety of climate studies and particularly subglacial water routing models. It is therefore necessary to ensure these interpretations are consistent and not affected by cross-track clutter. For the surface interface, interferometry and a family of methods relying on digital elevation models have been used to successfully discriminate cross-track surface clutter. Here we present how interferometry can be applied to the problem of basal clutter from cross-track bed topography. Our approach is based on a comparison of the differential phases of ambiguous reflectors that may represent bed clutter and the differential phase of a reflector in an adjacent area that appears unaffected by basal clutter. The reflector yielding the smallest interferometric phase difference relative to the unambiguous bed reflector is considered to represent its consistent continuation. We successfully demonstrate our approach using 60 MHz center frequency MARFA data collected over Devon Ice Cap in the Canadian Arctic. Finally, we investigate the effects of clutter-affected and interferometry-corrected bed interpretations on ice layer thickness estimates, basal hydraulic head gradients and the potential extent of inferred subglacial water bodies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".