Thin-layer effects in glaciological seismic amplitude-versus-angle (AVA) analysis: implications for characterising a subglacial till unit, Russell Glacier, West Greenland
Bibliographic record
Abstract
Abstract. Seismic amplitude-versus-angle (AVA) methods are a powerful means of interpreting the physical properties of subglacial material, although interpreting an AVA response is complicated in the case of a thinly-layered substrate. A layer thinner than one-quarter of the seismic wavelength is considered seismically "thin", and reflections from its bounding interfaces are perceived as a single event. Since a lodged (non-deforming) subglacial till can capped by a thin (metre-scale) cap of dilatant (deforming) till, serious misinterpretations can result if thin layer considerations are not honoured. AVA responses for layered subglacial tills are simulated: we model dilatant layers of thickness 0.1–3.0 m (up to a quarter-wavelength of our synthetic seismic pulse) overlying a lodged half-space, assigning typical acoustic impedance and Poisson's ratios to each. If thin layer effects are neglected, the AVA response to ultra-thin (<1.0 m) dilatant layers yields incompatible physical properties (acoustic impedance and Poisson's ratio indicating, respectively, a low- and high-porosity unit). We show an interpretative strategy that identifies thin layer effects and accurately quantifies the modelled acoustic impedance of lodged till from the composite AVA response. We apply this method to example seismic AVA data from the Russell Glacier outlet of the West Greenland Ice Sheet, in which characteristics of thin layer responses are evident. We interpret a stratified subglacial deposit, with upper and lower layers of high-porosity (<1.0 m thick, Poisson's ratio >0.492 ± 0.015) and low-porosity (acoustic impedance of 4.20–4.39 × 106 kg m−2 s−1) material, respectively assumed to represent dilatant and lodged tills. Thin layer considerations are strongly advised wherever seismic AVA analyses are used to quantify subglacial material properties.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".