Mapping faults in the laboratory with seismic scattering 2: the modelling perspective
Bibliographic record
Abstract
SUMMARY Peak delays of acoustic emission (AE) data from rock deformation laboratory experiments are sensitive to both sample heterogeneities and deformation-induced impedance contrasts inside the sample. However, the relative importance of stochastic heterogeneity and discontinuities is uncertain, as is the relationship between peak delays and applied stress and strain. In the companion paper, we presented and analysed peak delay data from AE recorded in a sandstone sample that was triaxially deformed to failure. Here, we simulate P–SV waveforms of dominant frequency 200 kHz in a 2-D isotropic, layered medium using realistic parameters derived from the laboratory experiments previously analysed. Our aim is to provide a physical interpretation of the laboratory findings and constrain the role of a proxy of the evolving fault zone on peak delays. We consider a 2-D fault zone embedded in a host material that simulates the fracture plane as a more compliant layer and allows us to numerically investigate variations in peak delay. Measurements of background parameters, including isotropic velocity and fault thickness were optimized using laboratory data via an evolutionary algorithm. Our simulations clarify that near-source peak delay observations are sensitive to the heterogeneity within zones of intense strain even when far-field approximations are not valid. This sensitivity manifests through the arrival of trapped waves within the layer that is coupled with multiple reflections from the sample boundaries. Substantial uncertainties remain on the possibility of inverting sample parameters with 2-D simulations and such complex physics. Our combined experimental and modelling study suggests that peak delays and coda parameters are sensitive to the heterogeneity caused by faulting and strain variations at different stages of fault-inducing slow deformation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".