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Record W4388328532 · doi:10.5772/intechopen.1003138

The Source Rupture Models and Seismogenic Structures of the Iran 2017 MW 7.3 Earthquake

2023· book-chapter· en· W4388328532 on OpenAlexafffund
Shutian Ma, Parisa Asgharzadeh, Dariush Motazedian

Bibliographic record

VenueIntechOpen eBooks · 2023
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeologySeismologyEpicenterInterplate earthquakeFault (geology)Intraplate earthquakeTectonics

Abstract

fetched live from OpenAlex

The 12 November 2017 MW 7.3 Iran earthquake was further studied. By analyzing Rayleigh-wave dispersion data, crustal models in the surrounding of the epicenter were obtained. It was found that there are high-velocity layers over a low-velocity zone. Using the obtained crustal models and a grid search procedure, the initial rupture depth of about 16.4 km and the rupture propagation velocity of about 1.62 km/s were retrieved. The source rupture models were established using the obtained rupture initial depth and the rupture velocity. The key features are as follows: The earthquake occurred on a shallow dip-angle fault, with ruptures spanning high-velocity layers in a depth range of approximately 7–25 km. A noteworthy observation from comparing crustal and rupture models is the existence of a low-velocity zone (layers) beneath the major rupture region (below about 25 km). It was realized that the seismogenic structure of this earthquake showed that high-velocity layers lay a low-velocity zone in the Zagros mountain seismic belt.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.226
Teacher spread0.182 · 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 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

Citations2
Published2023
Admission routes2
Has abstractyes

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