Bibliographic record
Abstract
Monitoring seismic activity in mines, produced by high stress faults in the vicinity of the mining operations, is an important issue for mine safety. A seismic event produces a short-time duration acoustic pressure wave that travels through the rock. This low-energy seismic activity in mines is typically referred to as microseismic events. The location of a microseismic event can be estimated using the pressure wave signals recorded at a set of sensors distributed throughout the mine. The classical process for locating a radiating source involves two steps: an estimation of the time difference of arrival between all sensor pairs followed by the localization, requiring the solution of a set of non-linear equations. This traditional localization process has limited success when applied to microseismic events, since they have a short-time duration, and thus generating accurate arrival time estimates is a challenging task in a noisy environment. An alternate approach to traditional localization, that avoids time-delay estimation, is to search over a grid of hypothesized source locations to find the one that best explains the observed measurements. Here, this approach is used with a performance function that is the greatest energy calculated from the sum of the sensor signals, each of which is time-shifted by an amount consistent with the hypothesized location of the event. The result is a very robust algorithm that works well with short-time duration signals and given the recent advances in low-cost computational power can be implemented in real time. The paper describes the location algorithm. Results are presented for computer generated signals as well as actual signals produced by a microseismic event that occurred one kilometer below the surface in a potash mine near Saskatoon, Saskatchewan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".