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Record W4309288284 · doi:10.1785/0220220264

Earthquake Delay and Rupture Velocity in Near-Field Dynamic Triggering Dictated by Stress-Controlled Nucleation

2022· article· en· W4309288284 on OpenAlexaff
Peng Dong, Rong Chen, Kaiwen Xia, Wei Yao, Zhigang Peng, Derek Elsworth

Bibliographic record

VenueSeismological Research Letters · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSeismologyNucleationGeologyShear (geology)Rayleigh waveEarthquake ruptureStress fieldPhase (matter)Wave velocityStress (linguistics)Seismic waveFault (geology)Wave propagationPhysicsPetrologyStructural engineeringEngineeringOptics

Abstract

fetched live from OpenAlex

Abstract Dynamic triggering of earthquakes by seismic waves generated by another earthquake is widely observed, while the underlying nucleation mechanisms remain unclear. We report here dynamically triggered earthquakes on laboratory faults with tightly constrained imaging of the triggering process. The arriving stress wave alters the contact state of the laboratory fault and initiates rupture nucleation in two distinct phases. The triggered rupture grows at a fraction of the shear-wave velocity (∼0.4CS) and then transits to a very slow velocity (∼0.1CS) before culminating into runaway shear. This intervening very slow rupture phase is present only for seismic ratios conducive to sub-Rayleigh ruptures and is notably absent for supershear events. Thus, the delay in triggering decreases to a minimum for triggered supershear ruptures, whereas it scales with the stress state for triggered sub-Rayleigh ruptures. These results may help explain key characteristics of delayed near-field dynamic triggering and provide a simple theoretical framework for dynamic triggering at greater distances.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.276
Teacher spread0.253 · 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.

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

Citations19
Published2022
Admission routes1
Has abstractyes

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