Improved Estimation of Glacial‐Earthquake Size Through New Modeling of the Seismic Source
Bibliographic record
Abstract
Abstract The number of gigaton‐sized iceberg‐calving events occurring annually at Greenland glaciers is increasing, part of a larger trend of accelerating mass loss from the Greenland Ice Sheet. Though visual observation of large calving events is rare, ∼60 glacial earthquakes generated by these calving events are currently recorded each year by regional and global seismic stations. An empirical relationship between iceberg size and M CSF , a summary measure of glacial‐earthquake size, was recently demonstrated by Olsen and Nettles (2019), https://doi.org/10.1029/2019JF005054 . However, M CSF is known to be sensitive to choices made in modeling the seismic source. We incorporate constraints on the seismic source from laboratory studies of calving and test multiple source time functions using synthetic and observed glacial‐earthquake waveforms. We find that a simple, fixed time function with a shape informed by laboratory results greatly improves estimates of earthquake size. The average ratio of estimated to true peak force values is 1.03 for experiments using our preferred source model, compared with an average of 0.3 for models used in previous studies. We find that maximum‐force values estimated from waveform modeling depend far less on model choices than does M CSF , and therefore prefer maximum force as a measure of glacial‐earthquake size. Using both synthetic and real data, we confirm a correlation between maximum force and iceberg mass. Our results support the possibility of developing useful scaling relationships between seismic observables and physical parameters controlling glacier calving.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".