Multi-component Rayleigh wave dispersion analysis for Vs-depth profiling of Glaciers
Bibliographic record
Abstract
Seismic ice velocity estimates provide quantitative constraints on glacial systems including ice thickness, englacial structure, and bedrock topography. Detailed velocity modeling using active-source seismic surveys on glaciers, however, is often challenged by sub-optimal survey acquisition design due to complex field logistics. This study explores new potential of such surveys for characterizing potentially heterogeneous seismic ice velocities by leveraging dispersive Rayleigh-wave responses recorded on three-component (3-C) receivers. We use synthetic models to study survey design, data conditioning, and improvements provided by multi-component data for dispersion analysis that inform estimates of vertical velocity profiles. We employ these learnings to optimize the accuracy of dispersion curves derived from a limited aperture, 3-C dataset acquired on the Saskatchewan Glacier in the Canadian Rocky Mountains. Our experiments suggest that when working with a limited number of geophones practitioners should: prioritize array length over finer receiver spacing; use shot points to infill receiver gaps; preprocess shot gather data to emphasize Rayleigh waves; and use supergathers to enhance signal-to-noise ratio and extend effective array aperture prior to building dispersion panels. Finally, we extract novel value from 3-C dispersion analysis by combining vertical- and horizontal-displacement data to reduce uncertainty and improve picked dispersion curve accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".