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Record W4308015747 · doi:10.1190/tle41110748.1

Implications of slow fault slip for hydraulic-fracturing-induced seismicity

2022· article· en· W4308015747 on OpenAlexafffund
David W. Eaton, Thomas S. Eyre

Bibliographic record

VenueThe Leading Edge · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsAlberta Science NetworkUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsSlip (aerodynamics)GeologySeismologyInduced seismicityEpisodic tremor and slipSeismic hazardInterferometric synthetic aperture radarSynthetic aperture radarTectonicsRemote sensingEngineering

Abstract

fetched live from OpenAlex

Abstract Since the discovery of slow-slip phenomena, scientific understanding of the behavior of active fault systems has been transformed significantly. It is now recognized that tectonic fault systems are characterized by a spectrum of slip behavior, from “regular” (stick-slip) earthquakes that radiate elastic wave energy and occur on a timescale of seconds, to slow-slip events with durations ranging from minutes to years. More recently, slow-slip phenomena have been observed and modeled in association with injection-induced seismicity. This includes evidence for predominantly slow fault slip during injection that triggered dynamic rupture elsewhere on a fault. In the case of hydraulic fracturing, slow-slip behavior is consistent with the frictional characteristics of faults in clay-rich rocks. A change in pore pressure or slip rate can cause a fault to transition from slow to unstable slip. Through real-time monitoring of slip-slip processes and, potentially, the development of operational adjustments to reduce the hazard of damaging ground motions, a better understanding of slow-slip processes could contribute to improved risk mitigation for induced earthquakes. Effective tools for direct observation of slow-slip processes include tiltmeters, strainmeters, global navigation satellite systems, interferometric synthetic aperture radar, and distributed fiber-optic sensing.

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 score0.621
Threshold uncertainty score0.504

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 studies0.0010.000
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 head0.043
GPT teacher head0.263
Teacher spread0.220 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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