Watching the Cryosphere Thaw: Seismic Monitoring of Permafrost Degradation Using Distributed Acoustic Sensing During a Controlled Heating Experiment
Bibliographic record
Abstract
Abstract Permafrost degradation is rapidly increasing in response to a warming Arctic climate, altering landscapes and damaging critical infrastructure. Solutions for monitoring permafrost thaw dynamics are essential to understand biogeochemical feedbacks as well as to issue warnings for hazardous geotechnical conditions. We investigate the feasibility of permafrost monitoring using permanently installed fiber‐optic seismic networks. We conducted a 2‐month seismic monitoring campaign during a controlled thaw experiment using a permanent surface orbital vibrator (SOV) and a 2D‐array of distributed acoustic sensing (DAS) cables, and observed significant (15%) shear‐wave velocity (Vs) reductions and approximately 2 m depression of the permafrost table beneath the heating zone. These observations were validated by time‐lapse horizontal‐to‐vertical spectral ratio (HVSR) analysis from three co‐located broadband seismometers. The combination of SOV and DAS provided unique seismic observations for permafrost monitoring at the field scale, as well as a basis for design and development of early warning systems for permafrost thaw.
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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 | 0.000 | 0.000 |
| 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".