Fault surface morphology as an indicator for earthquake nucleation potential
Bibliographic record
Abstract
Abstract Laboratory measurements can determine the potential for geologic materials to generate unstable (seismic) slip, but a direct relation between sliding behavior in the laboratory and physical characteristics observable in the field is lacking, especially for the phyllosilicate-rich gouges that are widely observed in natural faults. We integrated laboratory friction experiments with surface topography microscopy and demonstrated a quantitative correlation between frictional slip behavior and fault surface morphology of centimeter-scale samples. Our results show that striated, smooth fault surfaces were formed in experiments that exhibited stable sliding, whereas potentially unstable sliding was associated with rougher, isotropic fault surfaces. We interpret that frictional stability and fault surface morphology are linked via the evolution of asperity contacts on localized slip surfaces. If fault surface roughness obeys a fractal relationship over a large range of length scales, then we infer that the morphological characteristics observed in the laboratory could indicate the earthquake nucleation potential on natural fault surfaces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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) | 0.017 | 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 teacher head, 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".