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Record W1997752552 · doi:10.1080/13632460601031631

A Fast and Efficient Simulation of the Far-Fault and Near-Fault Earthquake Ground Motions Associated with the June 17 and 21, 2000, Earthquakes in South Iceland

2007· article· en· W1997752552 on OpenAlexfundno aff
Benedikt Halldórsson, Símon Ólafsson, Ragnar Sigbjörnsson

Bibliographic record

VenueJournal of Earthquake Engineering · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsnot available
FundersUniversity of Windsor
KeywordsSeismologyGeologyAttenuationFault (geology)Seismic gapInversion (geology)Earthquake simulationPhysicsTectonics

Abstract

fetched live from OpenAlex

The two Mw 6.5 earthquakes on June 17 and 21, 2000, respectively, in the populated South Iceland Seismic Zone (SISZ) significantly augmented the Icelandic database of strong ground motions, and several strong velocity pulses were recorded at near-fault sites. The strong motions are interpreted via the Specific Barrier Model (SBM) and a mathematical model of near-fault velocity pulses. The data indicates self-similar source scaling and significantly greater attenuation of seismic waves than in other interplate regions. Through inversion of the data a new attenuation function for the SISZ has been adopted, which results in unbiased simulations. For the first time, the characteristics of the recorded near-fault pulses have been identified and compared to the worldwide database of such records. The SBM and the near-fault pulse model combine naturally in a fast and efficient synthesis of realistic, broad-band strong ground motions in the far-fault and near-fault region. Such simulations are showcased for the June 2000 earthquakes and indicate that the modeling approach adopted in this study is an effective tool for the estimation of realistic earthquake ground motions in the SISZ.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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 designSimulation or modeling
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

Citations31
Published2007
Admission routes1
Has abstractyes

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