Machine Learning Aids Rapid Assessment of Aftershocks: Application to the 2022–2023 Peace River Earthquake Sequence, Alberta, Canada
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The adoption of machine learning (ML) models has ignited a paradigm shift in seismic analysis, fostering enhanced efficiency in capturing patterns of seismic activity with reduced need for time-consuming user interaction. Here, we investigate automated event detection and extraction of seismic phases using two widely used ML models: EQTransformer and PhaseNet. We applied both the models to four weeks of continuous recordings of aftershocks using a temporary array following the 30 November 2022, ML 5.6 earthquake near Peace River, Alberta, Canada. Both the tools identified >1000 events over the recording period. The aftershocks are located in close proximity to the ML 5.6 mainshock as well as to wastewater disposal operations that were ongoing at the time. Both the methods reveal an aftershock distribution that was not identified by the regional network; however, we find that events detected by PhaseNet have smaller event location errors and better depict subtle fault structures at depth, despite identifying ∼200 events less than EQTransformer. Our results highlight the advantages of using ML models for rapid detection and assessment of seismicity following felt events, which is important for rapidly assessing seismic hazard potential and risk.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it