Moment tensor and stress inversion solutions of acoustic emissions during compression and tensile fracturing in crystalline rocks
Bibliographic record
Abstract
We investigate the accuracy and robustness of moment tensor (MT) and stress inversion solutions derived from acoustic emissions (AEs) during the laboratory fracturing of prismatic Barre granite specimens. Pre-cut flaws in the specimens introduce a complex stress field, resulting in a spatial and temporal variation of focal mechanisms. Specifically, we consider two experimental setups: (1) where the rock is loaded in compression to generate primarily shear-type fractures and (2) where the material is loaded in indirect tension to generate predominantly tensile-type fractures. In each test, we first decompose AE moment tensors into double-couple (DC) and non-DC terms and then derive unambiguous normal and slip vectors using k-means clustering and an unstructured damped stress inversion algorithm. We explore temporal and spatial distributions of DC and non-DC events at different loading levels. The majority of the DC and the tensile non-DC events cluster around the pre-cut flaws, where macro-cracks later develop. Results of stress inversion are verified against the stress field from finite element (FE) modeling. A good agreement is found between the experimentally derived and numerically simulated stress orientations. To the best of the authors’ knowledge, this work presents the first case where stress inversion methodologies are validated by numerical simulations at laboratory scale and under highly heterogeneous stress distributions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".