Topological properties of aftershock clusters in a viscoelastic model of quasi-brittle failure
Bibliographic record
Abstract
Material failure at different scales and processes can be modeled as an emergent feature in terms of avalanche dynamics in micromechanical systems. Event-event triggering -or aftershocks- is common in seismological catalogs and acoustic emission experiments [1] among other phenomena. Stochastic branching and linear Hawkes processes are used to model the statistical properties of catalogs. In the micromechanical approach, viscoelastic stress transfer and after-slip are among the proposed mechanism of aftershocks. Here we ask this simple question: 'Do aftershock sequences in micromechanical models agree with such epidemic branching paradigm?' We introduce two fibrous models as prototypes of viscoelastic fracture [2] which (i) provides an analytical explanation to the acceleration of activity in absence of critical failure observed in acoustic emission experiments [3]; (ii) reproduce the typical spatio-temporal properties of triggering found in field catalogs, acoustic emission experiments; but (iii) display discrepancies with the branching topological properties predicted by stochastic models [4], probably due to physical constrains. [1] J. Baró et al., Phys. Rev. Lett. 110 (8), 088702 (2013). [2] J. Baró, J. Davidsen, Phys. Rev. E 97 (3), 033002 (2018). [3] J. Baró, et al., Phys. Rev. Lett. 120 (24), 245501 (2018). [4] S. Saichev, et al., Pure and App. Geoph. 162 (6), 1113-1134 (2005).
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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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".