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Record W2971636029 · doi:10.1029/2019gl084536

A Model of Shallow Viscoelastic Relaxation for Seismically Induced Tension Cracks in the Chile‐Peru Forearc

2019· article· en· W2971636029 on OpenAlexafffund
Haipeng Luo, Kelin Wang, Hiroki Sone, J. He

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsGeological Survey of CanadaUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsForearcGeologyTension (geology)SeismologyCrustSubmarine pipelineDeformation (meteorology)Relaxation (psychology)ViscoelasticitySubductionGeotechnical engineeringGeophysicsTectonicsMaterials scienceCompression (physics)Composite material

Abstract

fetched live from OpenAlex

Abstract Tension cracks were generated by past megathrust earthquakes along the coastal forearc of Chile‐Peru. To explain why elastic rebound in an offshore earthquake can cause widespread permanent deformation onshore, we propose a model in which the near‐surface material exhibits viscoelastic behavior, analogous to laboratory‐observed behavior of petroleum reservoir rocks. Because of near‐surface relaxation, interseismic deformation builds up stress only in the deeper crust. Elastic rebound of the deeper crust during an earthquake induces near‐surface tension to generate cracks. We numerically demonstrate the proposed mechanism using hypothetical and real megathrust earthquakes. The location of the zone of peak tension, assumed to be responsible for the crack generation, is controlled by downdip rupture termination. A rupture farther downdip or terminating more gradually causes the zone of peak tension to be farther landward and broader. The tension cracks thus may contain important information on megathrust rupture dynamics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.062
GPT teacher head0.295
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations3
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueGeophysical Research Letters→Same topicearthquake and tectonic studies→French-language works237,207→