Detection and relocation of earthquakes in the sparsely instrumented Mackenzie Mountains region, Yukon and Northwest Territories, Canada
Bibliographic record
Abstract
The Mackenzie Mountains are an actively uplifting and seismogenic arcuate thrust belt lying within the Northwest Territories and Yukon, Canada. Seismic activity in the region is poorly constrained due to a historically sparse seismograph distribution. In this study, new data are analyzed from the 40-station, ~875 km-long Mackenzie Mountains temporary network (Baker et al., 2020) crossing the Cordillera-Craton region adjacent to and within the Mackenzie Mountains, in conjunction with Transportable Array and other sparsely distributed arrays in the region. Data from approximately August 2016 – August 2018 are processed and compared to the sparse-network earthquake catalog records maintained by the USGS and Natural Resources Canada. Using algorithms developed by Kushnir et al. (1990), Rawles and Thurber (2015), and Roecker et al. (2006), signals are identified and subsequently associated across the network to note potential events, estimate phase onsets, and resolve hypocenter locations. This study improves the regional earthquake catalog by detecting smaller-magnitude earthquakes and lowering the regional magnitude of completeness from Mc = 2.5 to 1.9. Within the Mackenzie Mountains and immediately surrounding areas we find 524 new events and additionally recommend an updated location for 185 previously cataloged events. Our b-value computation for the updated catalog (0.916 ± 0.08) likely indicates a relatively high level of regional differential stress. We identify the spatial distribution of earthquakes in the Mackenzie Mountains as diffuse, and offer far-field stress transfer as a mechanism for producing widespread reverse faulting observed in the region. Further, we associate regional seismicity with tectonic activity in the context of known faults and orogenic provinces such as the Richardson Mountains.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".