An Assessment of Geophysical Survey Techniques for Characterising the Subsurface Around Glacier Margins, and Recommendations for Future Applications
Bibliographic record
Abstract
Geophysical surveys provide an efficient and non-invasive means of studying subsurface conditions in numerous sedimentary settings. In this study, we explore the application of three geophysical methods to a proglacial environment, namely ground penetrating radar (GPR), seismic refraction and multi-channel analysis of surface waves (MASW). We apply these geophysical methods to three glacial landforms with contrasting morphologies and sedimentary characteristics, and we use the various responses to assess the applicability and limitations of each method for these proglacial targets. Our analysis shows that GPR and seismic (refraction and MASW) techniques can provide spatially extensive information on the internal architecture and composition of moraines, but careful survey designs are required to optimise data quality in these geologically complex environments. Based on our findings, we define a number of recommendations and a potential workflow to guide future geophysical investigations in analogous settings. We recommend the initial use of GPR in future studies of proglacial environments to inform (a) seismic survey design and (b) the selection of seismic interpretation techniques. We show the benefits of using multiple GPR antenna frequencies (e.g., 25 and 100 MHz) to provide decimetre scale imaging in the near surface (e.g., < 15 m) while also enabling signal penetration to targets at up to ∼40 m depth (e.g., bedrock). This strategy helps to circumvent changes in radar signal penetration resulting from variations in substrate conductivity or abundant scatterers. Our study also demonstrates the importance of combining multiple geophysical methods together with ground-truthing through sedimentological observations to reduce ambiguity in interpretations. Implementing our recommendations will improve geophysical survey practice in the field of glacial geology and allow geophysical methods to play an increasing role in the interpretation of glacial landforms and sediments.
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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 | 0.030 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".