Tidal modulation and triggering of low‐frequency earthquakes in northern Cascadia
Bibliographic record
Abstract
Abstract We analyze the influence of Earth and ocean tides on the triggering of low‐frequency earthquakes (LFEs) in northern Cascadia using three LFE catalogs for southern Vancouver Island and Washington state from episodic tremor and slip events between 2003 and 2013. Sensitivities of LFE families to tidally induced fault normal stress, updip shear stress (UDSS), and corresponding time derivatives are computed and their geographic variability is mapped. We find localized areas showing higher sensitivity to UDSS than their surroundings, suggesting that tidal sensitivity depends on laterally heterogeneous physical properties such as variable pore fluid pressures and frictional properties along the plate interface. We observe that sensitivity of LFEs to UDSS rises dramatically from near zero on the first day of strong activity to a maximum ∼4 days later. In addition, the peak LFE rate transitions from a correlation with peak tidal shear stress rate to a correlation with peak tidal shear stress through large slow slip events. We identify 64 Rapid‐Tremor‐Reversals (RTRs) that start a few days after the main slip front. The RTRs have an average stress drop of ∼0.8 kPa and a majority (72%) occurs during periods of large positive UDSS. The combined observations imply that RTRs play an important role in slow slip processes and that modulation of creep rate due to tidal stress and tidal triggering of secondary events are jointly responsible for the observed tidal sensitivity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".