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Record W4210947010 · doi:10.1038/s41598-022-06129-3

InSAR data reveal that the largest hydraulic fracturing-induced earthquake in Canada, to date, is a slow-slip event

2022· article· en· W4210947010 on OpenAlexafffundabout
Thomas S. Eyre, Sergey Samsonov, Wanpeng Feng, Honn Kao, David W. Eaton

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsGeological Survey of CanadaNatural Resources CanadaUniversity of VictoriaUniversity of Calgary
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsSeismologyGeologyMagnitude (astronomy)Slip (aerodynamics)Interferometric synthetic aperture radarSeismometerMoment magnitude scaleEpisodic tremor and slipSubductionForeshockTectonicsScalingAftershockSynthetic aperture radar

Abstract

fetched live from OpenAlex

For tectonic earthquakes, slip rate spans a continuum from creep to supershear earthquakes, where slow slip events (SSEs) are important in releasing stress without radiating damaging seismic energy. Industrial-scale subsurface fluid injection has caused induced earthquakes, but the role of SSEs in fault activation is currently unclear. Ground-deformation observations, measured by satellite radar, show that SSEs up to magnitude 5.0 occurred during hydraulic fracturing (HF) operations in northwestern Canada, corroborated by reported deformation of the steel well casing. Although the magnitude 5.0 SSE exceeded the magnitude of the largest induced earthquake in this region (magnitude 4.55), it was undetected by seismograph networks. The observed SSEs occurred within a buried thrust belt and their magnitude and duration are consistent with scaling behavior of SSEs in unbounded natural systems, e.g. slab interfaces in subduction zones.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.040
GPT teacher head0.237
Teacher spread0.198 · 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 teacher head, not a consensus.

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

Citations55
Published2022
Admission routes3
Has abstractyes

Explore more

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