Induced or Natural? Toward Rapid Expert Assessment, with Application to the Mw 5.2 Peace River Earthquake Sequence
Bibliographic record
Abstract
Abstract Based on information available at the time, several questionnaire-based schemes have been developed to provide a qualitative assessment of whether a specific earthquake (or earthquake sequence) was likely induced by anthropogenic activities or is inferred to be natural. From a pragmatic perspective, the value of this assessment is arguably the greatest in the immediate aftermath of an event (hours to days), because it could then better serve to guide regulatory response. However, necessary information is often incomplete or uncertain, and there remains a lack of scientific consensus on the most distinctive attributes of induced (vs. natural) earthquake sequences. We present a case study of the Mw 5.2 Peace River earthquake sequence (Alberta, Canada), evaluated using two published frameworks for origin interpretation. The Alberta Energy Regulator initially considered the sequence to be natural, but a study published ~4 mo later came to the opposite interpretation. Prior to this publication, we convened a panel of experts who completed questionnaires as set out by the frameworks; results using both schemes indicate that experts believe the sequence was likely induced. Based on these expert responses, we critically evaluate information that was available publicly in the weeks to months following the mainshock on 30 November 2022; reassess the relative importance of various components of the questionnaires from a parsimonious, rapid-response perspective; and consider other types of information that could be critical for near-real-time assessment of whether an event was induced or natural.
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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 | 0.128 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".