Seismologically Observed Spatiotemporal Drainage Activity at Moulins
Bibliographic record
Abstract
Abstract Hydrology is important for glacier dynamics, but it is difficult to monitor the subsurface drainage systems of glaciers by direct observations. Since meltwater drainage generates seismic signals, passive seismic analysis has the potential to be used to monitor these processes. To study continuous seismic radiation from the drainage, we analyze geophone data from six stations deployed at the Kaskawulsh Glacier in Yukon, Canada, during the summer of 2014 using ambient noise cross‐correlation techniques. We locate the noise sources by backprojecting the amplitude of the cross correlation to the glacier surface. Most of the ambient noise sequences are found in two clusters, with each cluster located in the vicinity of a moulin identified at the surface. Stronger seismic radiation is observed during the day, consistent with expected variability in melt rates. We demonstrate that the sparse seismic network array with 2 km station separation has the ability to detect moulins within the array with a precision of 50 m. We confirm that seismic activity is correlated with air temperature, and thus, melt, on a diurnal timescale, and precipitation correlates with the activity at longer timescales. Our results highlight the potential of passive seismic observations for monitoring water flow into subglacial channels through moulins with an affordable number of seismic stations, but quantification of water flow rates still remains a challenge. The cross‐correlation backprojection technique described here can also potentially be applied to any localized source of ambient noise such as ocean noise, tectonic tremor, and volcanic tremor.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".