Deep Mine Wavefront Reconstruction Using True 3D Sparse Data
Bibliographic record
Abstract
Summary Extrapolation of seismic wavefields is useful for determining the location of the seismic source, as well as analysing how the wavefront propagates given different local conditions. One type of such extrapolation is known as reverse-time migration, which is used successfully for various modelling tasks. In a typical mine setting reverse-time migration struggles due to microseismic monitoring arrays being sparse. Sparsity of data makes it difficult to achieve accurate results from extrapolation, so a way of overcoming this is explored. Given the true 3-dimensional geometry within a microseismic array, a method of reconstructing a wavefront using sparse data is introduced. The radial component of each seismic trace is time-shifted to a reference radius, followed by an interpolation on the sphere with this reference radius. This densely sampled wavefront is then used as a starting point for reverse-time migration, and can thus be used to analyse wave characteristics of interest such as peak particle velocities and accelerations. This can lead to a better assessment of hazard, and ultimately a safer working environment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".