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Record W4404510774 · doi:10.1093/gji/ggae413

The effect of earthquake fault rupture kinematics on tsunami generation: a numerical study of real events

2024· article· en· W4404510774 on OpenAlexfundno aff
K. A. Sementsov, Toshitaka Baba, S. V. Kolesov, Yuichiro Tanioka, M. A. Nosov

Bibliographic record

VenueGeophysical Journal International · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsnot available
FundersCouncil for Science, Technology and InnovationSwine Innovation Porc
KeywordsGeologySeismologyKinematicsEarthquake ruptureFault (geology)Earthquake simulationTypes of earthquakeRemotely triggered earthquakesForeshockIntraplate earthquakeInterplate earthquakeTsunami earthquakeGeodesySeismic gapAftershockTectonics

Abstract

fetched live from OpenAlex

SUMMARY The study is devoted to the effect of the fault rupture kinematics in the earthquake source on tsunami generation. Sixteen events of years 1992–2021 are investigated. For each event, the kinematic tsunami source (bottom motion during the earthquake) and the static tsunami source (permanent bottom deformation) were calculated using the Finite Fault Models provided by the U.S. Geological Survey. For both sources, numerical tsunami simulations were carried out within the framework of linear long-wave theory. Comparison of the simulation results showed that in 10 out of 16 events, the energy of tsunami excited by the kinematic source is greater than that excited by the static source. The maximum energy amplification (9.1 per cent) is observed at the minimum ratio of average rupture velocity to long-wave velocity. The Illapel 2015 event has been investigated more thoroughly using dispersive tsunami models jagurs and cptm. This investigation showed that the kinematic source causes a spatial redistribution of tsunami amplitudes and a notable amplification of the high-frequency component in the time-series of tsunami height. At some points along the Chilean coast, the difference between the kinematic and static calculations is more than 2 m.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.014
GPT teacher head0.271
Teacher spread0.257 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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