The Effects of Cracks and Fluids on Post-Seismic Healing
Bibliographic record
Abstract
It is now well established that large seismic events change the surrounding velocities, and that these velocities slowly recover over time. Precisely which mechanisms control the recovery process are less well understood. We present the results of laboratory experiments to better characterise what properties of the underlying material control the recovery process. We do this by mixing two waves, one which perturbs the velocity of the sample (as an earthquake does in field data) and one which senses the change in velocity (as in changing noise correlations). This is an inherently nonlinear experiment as we mix two waves and measure the effects of this wave mixing. Within our experiments, we vary the properties of the samples to understand which are most important in controlling the nonlinear response. We focus on two mechanisms. The first is fractures and how changes in fracture properties change the nonlinear response. The second is fluids, in particular the effect of low saturations on the nonlinear response. By changing the fluids and fractures we can turn on and off the nonlinear mechanism, helping us to move toward a better understanding of the underlying mechanisms of these wave-wave interactions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".