Time-lapse GPR to quantify internal glacier deformation
Bibliographic record
Abstract
The estimation of surface flow velocities using satellite imagery, photogrammetry, or GPS data is now a standard practice in glaciology. In contrast, assessing internal ice deformation remains a significant challenge, often relying upon sparse measurements and theoretical models constrained by limited data. This study explores the potential of repeat, common-offset, ground-penetrating radar (GPR) reflection surveys as a tool to address this challenge. While GPR reflection data are traditionally utilized to determine glacier bed geometry, they also reveal key information about internal glacier structures, including the distribution of air pockets, debris, and water channels. Over time, these structures deform in response to glacier dynamics, suggesting that time-lapse GPR measurements could offer insights into internal flow velocities. In this regard, we propose a localized cross-correlation (LCC) approach, inspired by feature-tracking methods, as a starting point for a non-linear inversion of the deformation field. We test our methodology on synthetic GPR profile data, where electromagnetic wave propagation is modeled in a simplified flowing glacier containing randomly distributed scatterers, as well as on repeat GPR profiles acquired on the Findelen Glacier, Switzerland. In both cases, the GPR measurements are considered along the direction of glacier flow, and the corresponding data are diffraction enhanced and migrated prior to analysis. Our findings demonstrate that the proposed approach successfully retrieves the two-dimensional along-flow velocity field, highlighting its potential for field applications and future extension to three-dimensions.
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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 | 0.000 |
| 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) | 0.001 | 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".