Pressure and Stress Prediction in the Nankai Accretionary Prism: A Critical State Soil Mechanics Porosity‐Based Approach
Bibliographic record
Abstract
Abstract We predict pressure and stress from porosity in the Nankai accretionary prism with a critical state soil model that describes porosity as a function of mean stress and maximum shear stress, and assumes Coulomb failure within the wedge and uniaxial burial beneath it. At Ocean Drilling Program Sites 1174 and 808, we find that pore pressure in the prism supports 70% to 90% of the overburden (λu = 0.7 to 0.9), for a range of assumed friction angles (5–30°). The prism pore pressure is equal to or greater than that in the underthrust sediments even though the porosity is lower within the prism. The high pore pressures lead to a mechanically weak wedge that supports low maximum shear stress, and this in turn requires very low basal traction to remain consistent with the observed narrowly tapered wedge geometry. We estimate the décollement friction coefficient (μb) to be ~0.08–0.38 (ϕb′ = 4.6°–21°). Our approach defines a pathway to predict pressure in a wide range of environments from readily observed quantities (e.g., porosity and seismic velocity). Pressure and stress control the form of the Earth's collisional continental margins and play a key role in its greatest earthquakes. However, heretofore, there has been no systematic approach to relate material state (e.g., porosity), pore pressure, and stress in these systems.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".