Directional performance of an algorithm used to locate microseismic events in underground mines
Bibliographic record
Abstract
Locating local, low-intensity earthquakes, also referred to as microseismic events, in the vicinity of an underground mine is an important problem for both mine safety and mine planning. A microseismic event produces a finite time-duration acoustic signal that may have a small time-bandwidth product and passively locating such an event is a challenging task. Here a localization algorithm is described that hypothesizes a source location and aligns the sensor signals by removing the propagation delay for that hypothesized location. The hypothesized source location with the maximum energy, calculated from the sum of the aligned signals, is the estimate of the source position. This paper extends the algorithm to include signal weighting. An expression for the variance of location error is presented for the weighted algorithm and compared with the unweighted variance. A unique aspect of the variance expression is that it provides the performance in a direction specified by a three dimensional unit vector, e⃗, which is very useful for applications that restrict operations to tunnel structures, such as mining seismic and in-building acoustics.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".