Steadily propagating slip pulses driven by thermal decomposition
Bibliographic record
Abstract
Abstract Geophysical observations suggest that mature faults weaken significantly at seismic slip rates. Thermal pressurization and thermal decomposition are two mechanisms commonly used to explain this dynamic weakening. Both rely on pore fluid pressurization with thermal pressurization achieving this through thermal expansion of native solids and pore fluid and thermal decomposition by releasing additional pore fluid during a reaction. Several recent papers have looked at the role thermal pressurization plays during a dynamically propagating earthquake, but no previous models have studied the role of thermal decomposition. In this paper we present the first solutions accounting for thermal decomposition during dynamic rupture, solving for steady state self‐healing slip pulses propagating at a constant rupture velocity. First, we show that thermal decomposition leads to longer slip durations, larger total slips, and a distinctive along–fault slip rate profile. Next, we show that accounting for more than one weakening mechanism allows multiple steady slip pulses to exist at a given background stress, with some solutions corresponding to different balances between thermal pressurization and thermal decomposition, and others corresponding to activating a single reaction multiple times. Finally, we study how the rupture properties depend on the fault properties and show that the impact of thermal decomposition is largely controlled by the ratio of the hydraulic and thermal diffusivities χ = αhy/αth and the ratio of pore pressure generated to temperature rise buffered by the reaction Pr/Er.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".