MétaCan
Menu
Back to cohort
Record W2230994827 · doi:10.1093/gji/ggv365

Seismogenic faulting of the sedimentary sequence and laterally variable material properties in the Zagros Mountains (Iran) revealed by the August 2014 Murmuri (E. Dehloran) earthquake sequence

2015· article· en· W2230994827 on OpenAlexaff
Alex Copley, Ezgi Karasözen, Behnam Oveisi, John R. Elliott, Sergey Samsonov, Edwin Nissen

Bibliographic record

VenueGeophysical Journal International · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsNatural Resources Canada
FundersNatural Environment Research CouncilSight Research UK
KeywordsGeologySeismologyAftershockDécollementSequence (biology)Fault (geology)LayeringSedimentary rockThrust faultBasementSlip (aerodynamics)TectonicsPaleontology

Abstract

fetched live from OpenAlex

We present source models for the August 2014 Murmuri (Dehloran) earthquake sequence in the Zagros Mountains of Iran. An Mw6.2 mainshock was followed by an aftershock sequence containing five events of Mw ≥ 5.4. Models of P and SH waveforms show that all events had dominantly thrust-faulting mechanisms, and had centroid depths that place them within the thick sedimentary sequence, above the crystalline basement. The combination of our estimated focal mechanisms, relative relocations of the event hypocentres and the surface displacement patterns observed using InSAR imply that the mainshock and largest aftershock ruptured different fault planes and both contributed to the surface deformation. The fault planes both slipped in horizontally elongated patches, possibly due to rheological layering limiting the updip and downdip extent of rupture. The slip vector of the Murmuri mainshock implies that the decollement beneath the Lorestan Arc is weaker than any such feature beneath the Dezful Embayment, providing an explanation for the plan-view sinuosity of the range-front of the Zagros Mountains.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

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

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 head0.040
GPT teacher head0.240
Teacher spread0.200 · 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

Citations51
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueGeophysical Journal InternationalSame topicearthquake and tectonic studiesFrench-language works237,207