Modeling shear wave splitting due to stress‐induced anisotropy, with an application to Mount Asama Volcano, Japan
Bibliographic record
Abstract
Abstract We use numerical modeling to investigate the proposed stress‐based origin for changing anisotropy at Mount Asama Volcano, Japan. Stress‐induced anisotropy occurs when deviatoric stress conditions are applied to rocks which are permeated by microcracks and compliant pore space, leading to an anisotropic distribution of open crack features. Changes to the local stress field around volcanoes can thus affect the anisotropy of the region. The 2004 eruption of Mount Asama Volcano coincided with time‐varying shear wave splitting measurements, revealing changes in anisotropy that were attributed to stress changes associated with the eruption. To test this assertion, we create a model that incorporates knowledge of the volcanic stress, ray tracing, and estimation of the anisotropy to produce synthetic shear wave splitting results using a dyke stress model. Anisotropy is calculated in two ways, by considering a basic case of having uniform crack density and a case where the strength of anisotropy is related to dry crack closure from deviatoric stress. Our results show that this approach is sensitive to crack density, crack compliance, and the regional stress field, all of which are poorly constrained parameters. In the case of dry crack closure, results show that modeled stress conditions produce a much smaller degree of anisotropy than indicated by measurements. We propose that the source of anisotropy changes at Asama is tied to more complex processes that may precipitate from stress changes or other volcanic processes, such as the movement of pore fluid.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".