The State of Stress on the Fault Before, During, and After a Major Earthquake
Bibliographic record
Abstract
Earthquakes occur by overcoming fault friction; therefore, quantifying fault resistance is central to earthquake physics. Values for both static and dynamic friction are required, and the latter is especially difficult to determine on natural faults. However, large earthquakes provide signals that can determine friction in situ. The Japan Trench Fast Drilling Project (JFAST), an Integrated Ocean Discovery Program expedition, determined stresses by collecting data directly from the fault 1–2 years after the 2011 Mw 9.1 Tohoku earthquake. Geological, rheological, and geophysical data record stress before, during, and after the earthquake. Together, the observations imply that the shear strength during the earthquake was substantially below that predicted by the traditional Byerlee's law. Locally the stress drop appears near total, and stress reversal is plausible. Most solutions to the energy balance require off-fault deformation to account for dissipation during rupture. These observations make extreme coseismic weakening the preferred model for fault behavior. ▪ Determining the friction during an earthquake is required to understand when and where earthquakes occur. ▪ Drilling into the Tohoku fault showed that friction during the earthquake was low. ▪ Dynamic friction during the earthquake was lower than static friction. ▪ Complete stress drop is possible, and stress reversal is plausible.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".