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Record W3095159451 · doi:10.1029/2020gl090219

Sequential Fault Reactivation and Secondary Triggering in the March 2019 Red Deer Induced Earthquake Swarm

2020· article· en· W3095159451 on OpenAlexaffabout
Jingchuan Wang, Tianyang Li, Yu Jeffrey Gu, Ryan Schultz, Javad Yusifbayov, Miao Zhang

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsDalhousie UniversityAlberta EnergyGeological Survey of CanadaUniversity of Alberta
Fundersnot available
KeywordsSeismologySeismometerGeologyInduced seismicityFault (geology)Slip (aerodynamics)Stress fieldHypocenterGeophoneMicroseismEarthquake swarmSwarm behaviourStrike-slip tectonics

Abstract

fetched live from OpenAlex

Abstract Using data from nodal geophones and broadband seismometers, this study investigates the seismicity near Red Deer, Alberta, a region with increasing cases of hydraulic fracturing (HF)‐induced earthquakes. A cluster of 417 events was detected, and their spatial distribution and focal mechanisms reveal a NE trending rupture area with two strike‐slip fault planes. Reactivation of preexisting faults by pore pressure diffusion is likely responsible for the occurrence of the earthquake sequence following the ML 4.18 mainshock. The temporal sequence of reactivated fault orientations suggests apparent changes in the local stress field following the mainshock, which is also responsible for a remotely triggered cluster observed 1 month after the mainshock. This secondary triggering process enhances our understanding of the trailing effect of HF‐induced seismicity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.069
GPT teacher head0.297
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2020
Admission routes2
Has abstractyes

Explore more

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