High-accuracy relative event locations using a combined multiplet analysis and the double-difference inversion.
Bibliographic record
Abstract
In this work we apply the double-difference method to improve the accuracy of relative locations of microseismic events. First, we improve upon the P- and S-wave arrival times in an iterative way using crosscorrelation methods. Then we identify multiplets, or groups of events that have similar waveforms and source mechanisms, by crosscorrelation all events. Next, we apply the double-difference algorithm. The latter method minimizes the residuals between observed and predicted arrival times for pairs of microseismic events at each station, done by iteratively adjusting their differences. This relative location method is more appropriate for a dense cluster of events, hence we weight each observation based on the crosscorrelation coefficients and separation distances between events. This methodology also shows the different practical uses of normalized crosscorrelation functions for microseismic data analyses. Results are shown after applying this method to a microseismic data set from the mining industry, where a better linear feature is revealed after relocation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".