How Fault Rocks Form and Evolve in the Shallow San Andreas Fault
Bibliographic record
Abstract
Abstract We document the mechanical and geochemical processes of fault rock development in the shallow San Andreas fault (Mojave segment), and quantify their importance in shaping the mineralogy, grain size, fabric, and frictional characteristics of gouge. Through a combination of field and laboratory analysis of an extensive suite of shallow (<150 m) drill cores, we show that fault rocks evolved from a granodiorite protolith via three main processes: distributed microfracturing/pulverization; cataclastic flow and incipient fabric development; and production of authigenic illite/smectite during fluid‐rock interaction. The interdependence of these mechanical and geochemical processes results in a diverse suite of fault rocks, and causes significant changes in frictional strength. Spatial variations in the effects of these mechanisms, as manifested in fault‐rock mineralogy and geochemistry, indicate marked variations in their relative contribution to fault‐rock evolution. These data reveal a complex San Andreas fault with multiple principal slip zones and damaged and altered rock hosting numerous interconnected secondary slip surfaces. The resulting picture of the San Andreas Fault zone suggests a substantial departure from the simple structures envisioned for near‐surface seismogenenic faults in numerical models is required, and may inform future efforts to forecast peak ground accelerations during southern California earthquakes.
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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 | 0.000 |
| 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) | 0.001 | 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".