A micromechanical approach for simulating multiscale fabrics in large‐scale high‐strain zones: Theory and application
Bibliographic record
Abstract
Deformation fabrics in Earth's crust and mantle are commonly used to constrain the tectonic history, deformation mechanisms, and rheological properties of the lithosphere. Their formation involves heterogeneous and multiscale deformation processes that current single‐scale models cannot capture. Here we present a micromechanics‐based MultiOrder Power Law Approach (MOPLA) for the simulation of multiscale fabrics in crustal scale high‐strain zones. We consider the progressive deformation in a crustal high‐strain zone on three different scales. On the macroscopic scale, representing the average assemblage of rock units at a point, we regard the rock mass as a continuum made of many first‐order elements. The progressive deformation of first‐order elements in the macroscopic flow field simulates tectonic transposition. On the scale of an individual first‐order element, we regard it as an Eshelby inhomogeneity embedded in a poly element continuum. We apply Eshelby's inhomogeneity formalism for power law materials to relate the flow field inside a first‐order element to the macroscopic flow field. On the scale pertinent to structures observed on the outcrop or smaller scale, the partitioned flow fields inside individual first‐order elements are used to examine the fabric development. We implement MOPLA in MathCad, apply the approach to a natural example of the Cascade Lake shear zone, and discuss the implications of multiscale deformation. Our model predicts lineation patterns observed in natural high‐strain zones that have remained unexplained by single‐scale models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".