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Record W4225003216 · doi:10.1029/2022jb024091

Multistage Nucleation of the 2021 Yangbi M<sub>S</sub> 6.4 Earthquake, Yunnan, China and Its Foreshocks

2022· article· en· W4225003216 on OpenAlexaff
Min Liu, Hongyi Li, Lü Li, Miao Zhang, Weitao Wang

Bibliographic record

VenueJournal of Geophysical Research Solid Earth · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsDalhousie University
FundersNational Natural Science Foundation of China
KeywordsForeshockSeismologyGeologyNucleationSlip (aerodynamics)Fault (geology)AftershockPhysics

Abstract

fetched live from OpenAlex

Abstract On 21 May 2021, an M S 6.4 earthquake occurred in Yangbi, Yunnan province, China. A significant foreshock sequence occurred 3 days before the mainshock, which provides an opportunity to study earthquake nucleation. In this study, we adopt a template matching technique and a double‐difference location method to build a catalog for the foreshock sequence of the 2021 Yangbi M S 6.4 earthquake, which contains 1,086 events with high‐precision locations. The whole foreshock sequence can be grouped into four episodes, each with one M W &gt; 3.7 principal foreshock. The foreshocks within the first episode initiated on a NW‐trending small fault, whereas the subsequent foreshocks within episodes 2–4 jumped onto a nearby major fault to gradually rupture toward NW direction. We identify three M W &gt; 3.4 repeating earthquakes on the NW‐trending small fault in episode 1, implying that aseismic slip occurred in the early stage of the mainshock nucleation. Meanwhile, the rupture dimension estimation and stress disturbance analysis of the later foreshocks in episodes 2–4 reveal a cascading rupture process on the major fault, indicating that stress transfer may dominate the later stage of the mainshock nucleation. Hence, our study suggests that the two end‐member mechanisms of aseismic slip and stress transfer may be compossible in the nucleation process of the 2021 Yangbi M S 6.4 mainshock.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.771
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.027
GPT teacher head0.280
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.

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

Citations40
Published2022
Admission routes1
Has abstractyes

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