Estimation of Seismic Source Parameters Based on Focal Mechanisms During Stress-Relaxation in Rocks
Bibliographic record
Abstract
ABSTRACT: Stress-relaxation is a key process in the time-dependent behavior of rocks, influencing the stability of structures in rocks over long periods. This study focuses on estimating source parameters based on focal mechanisms during stress-relaxation experiments in crystalline rocks. The experiments were conducted on double-flawed prismatic Barre granite specimens under uniaxial compression in controlled laboratory conditions. Acoustic emission (AE) monitoring was employed to capture microseismic events, allowing for a detailed analysis of the focal mechanisms associated with the fracture propagation. Focal mechanisms, indicative of the type of fracture mode - non-shear or shear - were determined and analyzed about the stress relaxation process. Source parameters such as seismic moment, stress drop, source radius, and energy release were estimated using spectral fitting methods based on the displacement spectra from the non-shear and shear AE events. The temporal evolution of these parameters during relaxation at different stress levels was analyzed. Results revealed that during stress relaxation, non double-couple (NDC)-non shear events dominated in the early stages, with an increasing presence of double-couple (DC)-shear events as relaxation progressed. Higher values of the seismic moment, radiated seismic energy, and stress drop were observed for the DC events compared to the NDC events. This study provides a deeper understanding of the micromechanical processes driving time-dependent failure in rocks, which are critical for predicting the long-term behavior of rock masses in engineering applications.
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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".