Analysis of the Crooked Lake sequence near Fox Creek, Alberta: Comparison of a waveform correlation detection method to a traditional STA/LTA picker
Bibliographic record
Abstract
Summary We perform an in depth analysis of the Crooked Lake earthquake sequence occurring between November 29, 2013 and December 13, 2013 near Fox Creek, Alberta. A total of 24 events were detected during this time using a traditional STA/LTA triggering mechanism. We use the largest event, a magnitude 3.9, as a template event and perform waveform cross correlation to try and identify events not detected by the STA/LTA trigger. Over the fifteen day period the cross correlation detection method identified 113 locatable events including all of the 24 events detected by the STA/LTA trigger. We calculate the magnitude of completeness using a maximum curvature method (Wiemer and Wyss, 2000) and compare the result for the STA/LTA catalogue and the cross correlation catalogue. We observe a reduction of approximately 0.8 magnitude units in magnitude of completeness. Our results support previous findings (e.g. Gibbons and Ringdal, 2006) that cross correlation detection methods can offer significantly lower detection thresholds when compared to traditional triggering mechanisms. The improved detection ability results in a more complete catalogue that may provide an opportunity to gain information about the precursors to induced seismicity or facilitate efforts to image the structure of the area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".