Helicopter-borne ground-penetrating radar investigations on temperate alpine glaciers: A comparison of different systems and their abilities for bedrock mapping
Bibliographic record
Abstract
ABSTRACT On the basis of a large data set, comprising approximately 1200 km of profile lines acquired with different helicopter-borne ground-penetrating radar (GPR) systems over temperate glaciers in the western Swiss Alps, we have analyzed the possibilities and limitations of using helicopter-borne GPR surveying to map the ice-bedrock interface. We have considered data from three different acquisition systems including (1) a low-frequency pulsed system hanging below the helicopter (BGR), (2) a stepped frequency system hanging below the helicopter (Radar Systemtechnik GmbH [RST]), and (3) a commercial system mounted directly on the helicopter skids (Geophysical Survey Systems Incorporated [GSSI]). The systems showed considerable differences in their performance. The best results were achieved with the BGR system. On average, the RST and GSSI systems yielded comparable results, but we observed significant site-specific differences. A comparison with ground-based GPR data found that the quality of helicopter-borne data is inferior, but the compelling advantages of airborne surveying still make helicopter-borne data acquisition an attractive option. Statistical analyses concerning the bedrock detectability revealed not only large differences between the different acquisition systems but also between different regions within our investigation area. The percentage of bedrock reflections identified (with respect to the overall profile length within a particular region) varied from 11.7% to 68.9%. Obvious factors for missing the bedrock reflections included large bedrock depths and steeply dipping bedrock interfaces, but we also observed that internal features within the ice body may obscure bedrock reflections. In particular, we identified a conspicuous “internal reflection band” in many profiles acquired with the GSSI system. We attribute this feature to abrupt changes of the water content within the ice, but more research is required for a better understanding of the nature of this internal reflection band.
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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 | 0.001 |
| 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 | 0.000 | 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 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".