Determination of the pore fluid pressure ratio at seismogenic megathrusts in subduction zones: Implications for strength of asperities and Andean‐type mountain building
Bibliographic record
Abstract
We construct the differential stress profile across the fore arc in a subduction zone from the force balance between the shear stress, τ, at seismogenic megathrust and the lithostatic pressure. We assume that τ is written by μ (1 − λ) σn, where λ is the pore fluid pressure ratio, μ is the coefficient of static friction, and σn is the normal stress. Given a density structure of the fore‐arc wedge, we determine λ by comparing calculated fore‐arc stresses with observed ones, as 0.95–0.98 in Shikoku, Miyagi, Peru, north Chile, and south Chile and 0.90–0.93 in south Vancouver Island and Washington. The parameter τ averaged over the seismogenic megathrust is of the order of ∼10 MPa. Stress drops of great earthquakes in these zones occupy 14–87% and not a constant fraction of τ. They, on the other hand, increase linearly with 1 − λ. We propose a simple fault model in which the area of asperities as a fraction of the total fault area is proportional to 1 − λ. Variation of fractional area of asperities thus may explain the observed correlation and the regional variation of λ. Assuming that the differential stress at summit of the Andean mountains is zero, not at the coast as observed at present, we determine λ to be 0.84 in north Chile in the mountain building stage. Such a smaller value of λ, along with λ < ∼0.4 in collision zones previously obtained and >∼0.9 in subduction zones, would suggest that variation of λ controls the tectonic style of the Earth.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".