Source scaling relations and along‐strike segmentation of slow slip events in a 3‐D subduction fault model
Bibliographic record
Abstract
Abstract Scaling between slip event duration and equivalent moment is diagnostic of the underlying physics of rupture process. For episodic slow slip events (SSEs) in subduction zones, their moment‐duration relation is not clearly defined and shows considerable variation among individual margins where multiple episodes of SSEs are geodetically detected. Here I set up a 3‐D planar thrust fault model within the framework of rate‐state friction to study the spatiotemporal evolution of slip and stress during SSEs. SSE source properties, including along‐strike length, duration, equivalent moment, and stress drop, are quantified, and their scaling relations are compared to observations. Modeled SSEs have nearly constant stress drops of 0.001 to 0.01 MPa, due to near‐lithostatic pore pressure at the SSE depths. The modeled moment‐duration scaling is between 1 (linear) and 2 (squared). When multiple SSEs appear simultaneously along the strike, the stress interaction between approaching slip fronts results in higher average propagation speeds for longer lengths, which leads to a scaling close to 2. When SSEs are well offset in space and time, stress interaction is negligible and the scaling is close to 1. Two types of SSE along‐strike segmentation gaps are identified from model results. The “primary” gaps are persistent segmentation boundaries due to along‐strike variation of effective normal stress, while the “secondary” gaps evolve through SSE cycles and reflect the stress condition within a primary segment. This implies that some geodetically inferred SSE segmentation boundaries may result from slower slip velocities below the resolution limits of geodetic inversion models.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".