On the Unloading‐Induced Fault Reactivation: The Effect of Stress Path on Failure Criterion and Rupture Dynamics
Bibliographic record
Abstract
Abstract Fault reactivations induced by deep excavation can pose significant challenges to underground construction or resource extraction. Laboratory experiments on rock faults demonstrate that unloading‐induced fault reactivations obey the Coulomb failure criterion derived from loading‐induced events. However, the effect of stress path during unloading on the failure criterion and rupture dynamics of fault reactivations remains poorly understood. Here, we present findings from a series of laboratory experiments aimed at elucidating the effect of the unloading path on the failure criterion and rupture dynamics of fault reactivations. We conducted experiments under various stress conditions, examining two cases of unloading paths. In Case I, we unloaded the minimum principal stress, while in Case II, the maximum principal stress was unloaded. Strain gauges and high‐speed photography were employed to capture the transient dynamic rupture process. Our investigations have yielded new insights into the effect of unloading path on the rupture dynamics when the fault is reactivated. In Case I, we observed fault reactivations resembling those loading‐induced events characterized by forward sliding. Conversely, in Case II, fault reactivations associated with stress reversal produce mild reversed sliding with lower stress drop and rupture velocity. Furthermore, we find that there is a remarkable reduction in static friction for reversed sliding, indicating that the failure criterion for fault reactivation is influenced by the stress path. We demonstrate that enhanced stress heterogeneity, caused by stress reversal, serves as a mechanism for reduced static friction. These findings contribute to our understanding of the mechanisms underlying fault reactivations, particularly those involving reversed sliding.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".