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Record W2887424155 · doi:10.1029/2018gl079027

Faults and Non‐Double‐Couple Components for Induced Earthquakes

2018· article· en· W2887424155 on OpenAlexafffundabout
Ruijia Wang, Yu Jeffrey Gu, Ryan Schultz, Yunfeng Chen

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsAlberta EnergyGeological Survey of CanadaUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Geological Survey
KeywordsInduced seismicityGeologySeismologyTectonicsHydraulic fracturingThrust faultFault (geology)Focal mechanismSlip (aerodynamics)Structural basinGeomorphologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Focal mechanisms of induced earthquakes reflect anthropogenic contributions to preexisting geological features and fault slippages. In this paper, we examine fault‐related (double‐couple (DC)) and possibly fluid‐related (non‐double‐couple (non‐DC)) mechanisms of induced earthquakes ( M 2–6) at regional scales. We systematically compare well‐resolved focal mechanisms of 33 events in the Western Canada Sedimentary Basin, among which 12 were induced by hydraulic fracturing and one by secondary recovery. Most of the seismicity is dominated by strike‐slip/thrust faulting regimes, whereas limited (but consistent) non‐DC components are obtained from injection‐induced seismicity in central Alberta. We interpret the persistent compensated‐linear‐vector‐dipole components ( M 2.1–3.8) as reflecting fracture growth and/or noncoplanar faults slippages during hydraulic‐fracturing stimulations. We further expand the moment tensor decomposition analysis to four representative classes of induced seismicity globally and find that the overall contribution of non‐DC components is comparable between induced and tectonic earthquakes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.885

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.086
GPT teacher head0.325
Teacher spread0.238 · 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

Citations80
Published2018
Admission routes3
Has abstractyes

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