Probing the Nature of Low-Frequency Earthquakes Through the Deconvolution of Tectonic Tremor
Bibliographic record
Abstract
We resolve the low-frequency earthquake (LFE) activity within tremor from a compact area beneath southern Vancouver Island. Using LFE templates made from stacking thousands of nearly co-located LFEs as empirical Green’s functions, we detect LFEs using time-domain iterative deconvolution of the ”optimal” horizontal component (the dominant particle motion direction) of tremor velocity seismograms. The deconvolution is guided by the correlation between seismic stations. During identified tremor bursts, our 3-station catalog yields more than one detection per second. 60% of these are validated at a fourth station, but judging from the consistency of their migration patterns, many of those rejected are also legitimate. Waveforms predicted from these detections simultaneously reduce the residual on the unused vertical and ”orthogonal” horizontal components of the seismograms. The unresolved portions of the seismograms are likely times of significant tremor activity outside the target area. Deconvolution reveals clear LFE migrations with speeds near 5-6 m/s during 20% of the bursts, consistent with contemporaneous 4-s tremor detections in the same region. Given the number of consecutive seismogram peaks with detections, it is doubtful that each detection represents just one LFE. More likely, tremor is usually saturated in time with LFEs that are on average separated by less than the characteristic LFE duration. Such time saturation is consistent with stochastic models of tremor generation designed to explain tremor spectra, and also disguises the real number of LFEs within tremor, making it difficult to constrain the seismic moment and duration of individual LFEs.
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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 | 0.000 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".