Silica gel formation during fault slip: Evidence from the rock record
Bibliographic record
Abstract
Dynamic reduction of fault strength is a key process during earthquake rupture. Many mechanisms for causing coseismic weakening have been proposed based on theory and laboratory experiments, including silica gel lubrication. However, few have been observed in nature. Here we report on the first documented occurrence of a natural silica gel coating a fault surface. The Corona Heights fault slickenside in San Francisco, California, is covered by a shiny layer of translucent silica. Microstructures in this layer show flow banding, armored clasts, and extreme comminution compared to adjacent cataclasites. The layer is composed of ̃100 nm to 1 μm grains of quartz, hydrous crystalline silica, and amorphous silica, with 10-100 nm inclusions of Fe oxides and ellipsoidal silica colloids. Kinematic indicators and mixing with adjacent cataclasites suggest the shiny layer was fluid during fault slip. The layer therefore represents a relict silica gel that formed during fault motion, and which could have resulted in frictional instability. These observations confirm that the silica gels formed in rock friction experiments do occur in natural faults and therefore that silica gel formation can act as a dynamic weakening mechanism in faults at shallow crustal conditions. © 2013 Geological Society of America.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".