Laboratory observations of time‐dependent frictional strengthening and stress relaxation in natural and synthetic fault gouges
Bibliographic record
Abstract
Abstract Interseismic recovery of fault strength (healing) following earthquake failure is a fundamental requirement of the seismic cycle and likely plays a key role in determining the stability and slip behavior of tectonic faults. We report on laboratory measurements of time‐ and slip‐dependent frictional strengthening for natural and synthetic gouges to evaluate the role of mineralogy in frictional strengthening. We performed slide‐hold‐slide (SHS) shearing experiments on nine natural fault gouges and eight synthetic gouges at conditions of 20 MPa normal stress, 100% relative humidity (RH), large shear strain (~15), and room temperature. Phyllosilicate‐rich rocks show the lowest rates of frictional strengthening. Samples rich in quartz and feldspar exhibit intermediate rates of frictional strengthening, and calcite‐rich gouges show the largest values. Our results show that (1) the rates of frictional strengthening and creep relaxation scale with frictional strength, (2) phyllosilicate‐rich fault gouges have low strength and healing characteristics that promote stable, aseismic creep, (3) most natural fault gouges exhibit intermediate rates of frictional strengthening, consistent with a broad range of fault slip behaviors, and (4) calcite‐rich fault rocks show the highest rates of frictional strengthening, low values of dilation upon reshear, and high frictional strengths, all of which would promote seismogenic behavior.
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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 | 0.000 |
| 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) | 0.000 | 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
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