Characteristics of microseismicity in the Kiskatinaw area, northeastern British Columbia, Canada
Bibliographic record
Abstract
Since the early 2000s, lots of induced earthquakes have occurred due to fluid-injection during the development of unconventional resources at the Kiskatinaw Seismic Monitoring and Mitigation Area (KSMMA) located in northeastern British Columbia, Canada. The spatial-temporal distribution of microearthquakes induced by fluid-injection are important to understand the characteristics of crack and movement of fluid. Also, to mitigate earthquake disasters, it is essential to continuously monitor microearthquakes in fluid-injection areas. We used the seismic data recorded at the EON-ROSE seismic network, which is a dense seismic network consisting of 16 broadband seismic stations, and GSC-BCOGC seismic network to analyze the characteristics of microseismicity of the KSMMA in 2020. We detected the seismic signal (P- and S-wave) using the automatic seismic phase detection method, which is based on the short-term-average to long-term-average ratio (STA/LTA) and kurtosis. And then, we associated the seismic phase arrival data to combine to earthquakes from the automatic seismic phase association method using the temporal distribution of the detected signals and the spatial distribution of the seismic stations used. The hypocenter parameters of associated earthquakes were determined with the HYPOINVERSE location algorithm and the existing 1-D velocity model of KSMMA. The epicenter distributions of the detected earthquakes are concentrated in the area known as active fluid-injection, and the focal depths are also distributed at about 2 km. We analyzed the seismicity by dividing it with three periods based on COVID-19 lockdown and confirmed the low-seismicity of the lockdown period, which is consistent with the result of the independent study performed at the region.
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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 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".