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Record W4379619395 · doi:10.5772/intechopen.110728

New Insights into Major Seismic Events by Coulomb Stress Change Pattern and Aftershock Distributions – Implication for Active Tectonics

2023· book-chapter· en· W4379619395 on OpenAlexfundno aff
Mahnaz Nedaei

Bibliographic record

VenueIntechOpen eBooks · 2023
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchU.S. Geological SurveyNational Aeronautics and Space Administration
KeywordsSeismologyAftershockGeologyFault (geology)Fault planeCoulombActive faultSeismic hazardFocal mechanismSlip (aerodynamics)TectonicsPhysics

Abstract

fetched live from OpenAlex

Identification of the fault plane of earthquakes can be a critical contribution of seismology to regional tectonic studies and assessment of expected deformation and damage patterns. A fundamental ambiguity in the representation of an earthquake with a focal mechanism is to recognize the causative fault plane accommodating the slip during the event among the two nodal planes. The Coulomb static stress has been commonly used to determine the stress distribution induced by an event. However, for the first time in this research, the Coulomb regional stress was resolved on nodal planes to realize the optimally oriented plane for failure having maximum Coulomb stress on which the regional stress triggers an event. The method has been conducted for the April 5th, 2017 Sefidsang earthquake in NE Iran. The results reveal that the earthquake-triggering fault is a northeast-dipping listric fault with dextral reverse movement. The identified structural aspects subjected to active deformation in the area have crucial implications for seismic hazard assessment of the region and potential future failure areas.

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: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.247
Teacher spread0.220 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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