Complex networks and waveforms from acoustic emissions in laboratory earthquakes
Bibliographic record
Abstract
Abstract. Understanding the physics of acoustic excitations emitted during the cracking of materials is one of the long-standing challenges for material scientists and geophysicists. In this study, we report novel results of applications of functional complex networks on acoustic emission waveforms emitted during the evolution of frictional interfaces. Our results show that laboratory faults at microscopic scales undergo a sequence of generic phases, including strengthening, weakening or fast slip and slow slip, leading to healing. For the first time we develop a formulation on the dissipated energy due to acoustic emission signals in terms of short-term and long-term features (i.e., networks' characteristics) of events. We illuminate the transition from regular to slow ruptures. We show that this transition can lead to the onset of the critical rupture class similar to the direct observations of this phenomenon in the transparent samples. Furthermore, we demonstrate the detailed submicron evolution of the interface due to the short-term evolution of the rupture tip. As another novel result, we find that the nucleation phase of most amplified events follows a nearly constant timescale, corresponding to the initial strengthening or locking of the interface. This likely indicates that a thermally activated process can play a crucial role near the moving crack tip.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".