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Record W3042146598 · doi:10.1785/0120190261

A Study on the Largest Hydraulic-Fracturing-Induced Earthquake in Canada: Observations and Static Stress-Drop Estimation

2020· article· en· W3042146598 on OpenAlexaffabout
Bei Wang, R. M. Harrington, Yajing Liu, Honn Kao, Hongyu Yu

Bibliographic record

VenueBulletin of the Seismological Society of America · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsMcGill UniversityUniversity of VictoriaGeological Survey of Canada
Fundersnot available
KeywordsSeismologyHypocenterGeologySeismometerHydraulic fracturingMoment magnitude scaleMagnitude (astronomy)MicroseismSeismic momentMaximum magnitudeSeismic hazardTectonicsFault (geology)Geotechnical engineeringInduced seismicity

Abstract

fetched live from OpenAlex

ABSTRACT On 17 August 2015, an Mw 4.6 earthquake occurred northwest of Fort St. John, British Columbia, possibly induced by hydraulic fracturing (HF). We use data from eight broadband seismometers located ∼50 km from the hypocenter to detect and estimate source parameters of more than 300 events proximal to the mainshock. Stress-drop values estimated using seismic moment and corner frequency from single-event spectra and spectral ratios range from ∼1 to 35 MPa, within the typical range of tectonic earthquakes. We observe an ∼5-day delay between the onset of fluid injection and the mainshock, a b-value of 0.78 for the sequence, and a maximum earthquake magnitude larger than the prediction based on the total injection volume, suggesting that the Mw 4.6 sequence occurred on a pre-existing fault and that the maximum magnitude is likely controlled by tectonic conditions. Results presented here show that pre-existing fault structures should be taken into consideration to better estimate seismic hazard associated with HF operations and to develop schemes for risk mitigation in close proximity to HF wells.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.652
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

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

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 head0.045
GPT teacher head0.218
Teacher spread0.173 · 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.

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

Citations44
Published2020
Admission routes2
Has abstractyes

Explore more

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