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Record W2119423854 · doi:10.1002/grl.50298

Numerical simulations on megathrust rupture stabilized under strong dilatancy strengthening in slow slip region

2013· article· en· W2119423854 on OpenAlexafffund
Yajing Liu

Bibliographic record

VenueGeophysical Research Letters · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill UniversityWoods Hole Oceanographic InstitutionNational Science Foundation
KeywordsSlip (aerodynamics)GeologyDilatantEpisodic tremor and slipSubductionSeismologyFault (geology)Geotechnical engineeringTectonics

Abstract

fetched live from OpenAlex

Episodic slow slip events (SSEs) typically involve a few millimeters to centimeters of slip over several days to months at depths near or further downdip of megathrust seismogenic zones. Despite its widespread presence in subduction margins, it remains unknown how SSEs interact with the seismogenic zone and affect megathrust ruptures. Here, I construct a 2‐D thrust fault model governed by rate‐state friction to investigate how fault dilatancy influences the amplitude and spatial distribution of‘ coseismic slip, afterslip, and SSEs. Model results illustrate that, under strong dilatancy and high pore pressure around the friction stability transition, coseismic rupture stops at the onset of SSEs. Modeled SSEs have lower velocities, longer recurrence intervals and durations, and larger slip amounts as dilatancy becomes stronger, demonstrating a transition from short‐term to long‐term type of SSE behavior. These results qualitatively explain the range of spatial distributions of SSEs and megathrust ruptures observed or inferred in natural subduction zones. Furthermore, the relative depths of SSEs and megathrust afterslip may serve as an indicator of dilatancy effectiveness.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.054
GPT teacher head0.299
Teacher spread0.244 · 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; both teacher heads agree on what is shown here.

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

Citations65
Published2013
Admission routes2
Has abstractyes

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