Rupture Dynamics of Cascading Earthquakes in a Multiscale Fracture Network
Bibliographic record
Abstract
Abstract Fault‐damage zones comprise multiscale fracture networks that may slip dynamically and interact with the main fault during earthquake rupture. Using 3D dynamic rupture simulations and scale‐dependent fracture energy, we examine dynamic interactions of more than 800 intersecting multiscale fractures surrounding a listric fault, emulating a major listric fault and its damage zone. We investigate 10 distinct orientations of maximum horizontal stress, probing the conditions necessary for sustained slip within the fracture network or activating the main fault. Additionally, we assess the feasibility of nucleating dynamic rupture earthquake cascades from a distant fracture and investigate the sensitivity of fracture network cascading rupture to the effective normal stress level. We model either pure cascades or main fault rupture with limited off‐fault slip. We find that cascading ruptures within the fracture network are dynamically feasible under certain conditions, including: (a) the fracture energy scales with fracture and fault size, (b) favorable relative pre‐stress of fractures within the ambient stress field, and (c) close proximity of fractures. We find that cascading rupture within the fracture network discourages rupture on the main fault. Our simulations suggest that fractures with favorable relative pre‐stress, embedded within a fault damage zone, may lead to cascading earthquake rupture that shadows main fault slip. We find that such off‐fault events may reach moment magnitudes up to M w ≈ 5.5, comparable to magnitudes that can be otherwise hosted by the main fault. Our findings offer insights into physical processes governing cascading earthquake dynamic rupture within multiscale fracture networks.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".