Bibliographic record
Abstract
Viscous creep in the crust and mantle can have important consequences for earthquake patterns in time and space. Here, numerical models of repeating earthquake rupture are used to investigate how viscoelasticity can affect size distributions of repeated earthquakes on vertical, planar fault segments. The earthquake model is based on exact solutions of static 3-D elasticity theory. Viscous flow beneath the seismogenic crust is modelled as a Maxwell relaxation process. Two different fault strength models are used: a smooth rate- and state-dependent friction model and a strongly heterogeneous asperity model. In a previous paper it was shown that the characteristic scale of fault segmentation is proportional to the vertical width of a seismogenic fault. For both the heterogeneous and smooth friction models, viscous relaxation of the substrate modifies the spatio-temporal distribution of cumulative slip and slip events. For the heterogeneous models, the resulting quake size distributions are independent of the viscous relaxation time. In contrast, for the smooth models, it is shown that the characteristic event size is inversely proportional to the relaxation time of the substrate. These results imply a connection between the spatial complexity of fault zones and the maximum earthquake size a fault can sustain. Whereas earthquake sizes on complex fault zones depend only weakly on the viscous rheology beneath the seismogenic crust, the sizes of large events on relatively simple faults can be substantially increased due to viscous relaxation and partial decoupling of the seismogenic crust from the deeper crust and mantle.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".