The thickness of subduction plate boundary faults from the seafloor into the seismogenic zone
Bibliographic record
Abstract
The thickness of an active plate boundary fault is an important parameter for understanding \nthe strength and spatial heterogeneity of fault behavior. We have compiled direct \nmeasurements of the thickness of subduction thrust faults from active and ancient examples \nobserved by ocean drilling and fi eld studies in accretionary wedges. We describe a general \ngeometric model for subduction thrust décollements, which includes multiple simultaneously \nactive, anastomosing fault strands tens of meters thick. The total thickness encompassing all \nsimultaneously active strands increases to ~100–350 m at ~1–2 km below seafl oor, and this \nthickness is maintained down to a depth of ~15 km. Thin sharp faults representing earthquake \nslip surfaces or other discrete slip events are found within and along the edges of the tens-ofmeters- \nthick fault strands. Although fl attening, primary inherited chaotic fabrics, and fault \nmigration through subducting sediments or the frontal prism may build mélange sections that \nare much thicker (to several kilometers), this thickness does not describe the active fault at \nany depth. These observations suggest that models should treat the subduction thrust plate \nboundary fault as <1–20 cm thick during earthquakes, with a concentration of postseismic \nand interseismic creep in single to several strands 5–35 m thick, with lesser distributed interseismic \ndeformation in stratally disrupted rocks surrounding the fault strands.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".