Spatiotemporal evolution of slow slip events in a nonplanar fault model for northern Cascadia subduction zone
Bibliographic record
Abstract
Abstract Slow slip events (SSEs) are identified as the quasi‐stable fault deformation in the deep transition zone from locked to continuous sliding in many subduction zones. In the well‐instrumented Cascadia margin, a class of Mw6.0 slow slip events arise beneath Port Angeles every ∼14 months, as inferred from two decades of continuous geodetic monitoring. The along‐strike bending of the incoming oceanic plate beneath north Washington is a unique geometric feature whose influence on slow slip processes is still unknown. Here we incorporate a realistic fault geometry of northern Cascadia in the framework of rate‐ and state‐dependent friction law, to simulate the spatiotemporal evolution of slow slip events on a nonplanar subduction fault. The modeled SSEs capture the major characteristics revealed by GPS observations. The central 150 km long fault segment beneath Port Angeles acts as a repetitive slip patch, where SSEs appear every ∼1.5 years with a maximum slip of ∼2.5 cm. Two minor slip patches with smaller areas and cumulative slips straddle this central slip patch. The along‐strike segmentation of slow slip is inversely related to the local fault dip and strike angles of the slow slip zone, suggesting strong geometrical control on the slow slip process. This correlation holds even after removing the effect of W/h∗, ratio between velocity‐weakening SSE fault width and characteristic nucleation size. Besides the GPS‐detectable fast‐spreading phase, we find that each SSE cycle consists of deep pre‐SSE preparation and post‐SSE relaxation phases, which may be the driving mechanism for the deep tremor activity between major SSE episodes discovered in Cascadia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".