Understanding seismic velocity variations of subsea permafrost: A sensitivity study
Bibliographic record
Abstract
ABSTRACT Monitoring the change in permafrost conditions and distribution is crucial for forecasting global warming. As seismic velocities increase with ice content, marine seismic surveys can map the top of ice-bearing subsea permafrost on a large scale. However, conventional seismic methods cannot fully map internal velocity variations linked to changes in ice content because the removal of guided waves and multiples is challenging for reflection processing. Nevertheless, these arrivals carry information about velocity variations with depth. We investigate if a joint analysis of various wave arrivals could provide information about velocity variations within permafrost and accurately estimate the permafrost thickness. Through a sensitivity analysis, we estimate the feasibility of using different wave arrivals, such as reflections, refractions, multiples, and guided waves, to better characterize permafrost conditions. We find that guided waves are sensitive to velocity variations within permafrost and can detect the depth of the permafrost’s base under certain geologic conditions. Then, we combine the analysis of all seismic arrivals to derive a subsurface permafrost model for a seismic line collected in the Beaufort Sea. The joint analysis reveals the transitions in depth between ice-bonded and partially ice-bonded permafrost and offers a crude estimate of permafrost thickness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".