A Modelling Study Of Broadband Seismic Wave Propagation In A Deep Mine
Bibliographic record
Abstract
Summary Understanding the propagation of seismic waves in a presence of very strong elastic contrasts, such as topography, tunnels and ore-bodies is still a challenge. Safety in mining is a major concern and seismic monitoring is the main tool here. For engineering purposes, amplitudes (peak particle velocity/acceleration) and travel times of seismic events (mostly blasts or microseismic events) are critical parameters that have to be determined at various locations in a mine. These parameters are useful in preparing risk maps or to better understand the process of spatial and temporal stress distributions in a mine. Simple constant velocity models used for mine monitoring, cannot explain the observed complexities in scattered seismic waves. In hard-rock environments modelling of elastic seismic wavefield require detailed 3D petrophysical, infrastructure and topographical data to simulate the propagation of seismic wave with a frequencies up to few kilohertz. In this study, the effects of strong elastic contrasts on the propagation of broadband seismic waves will be illustrated using 2D/3D finite difference method.
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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".