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Record W2578986756 · doi:10.1002/2016ja023727

GPS detection of ionospheric Rayleigh wave and its source following the 2012 Haida Gwaii earthquake

2017· article· en· W2578986756 on OpenAlexaboutno aff
Shuanggen Jin, Rui Jin, Dan Li

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyIonosphereTotal electron contentGeodesyRayleigh waveSeismologyAmplitudeEpicenterWaveformTECGeophysicsSurface wavePhysicsOptics

Abstract

fetched live from OpenAlex

Abstract The processes and sources of seismo‐ionospheric disturbances are still not clear. In this paper, coseismic ionospheric disturbances (CIDs) are investigated by dual‐frequency GPS observations following the M w = 7.8 earthquake as results of the oblique‐thrust fault in the Haida Gwaii region, Canada, on 28 October 2012. Results show that the CIDs with an amplitude of up to 0.15 total electron content units (TECU) are found with spreading out at 2.20 km/s, which agree well with the Rayleigh wave propagation speed at 2.22 km/s detected by the bottom pressure records at about 10 min after the onset. The CIDs are a result of the upward propagation acoustic waves trigged by the Rayleigh wave in sequence from near field to far field. The strong correlation is found between the CIDs and the vertical ground motion recorded by seismometers nearby the epicenter. The total electron content (TEC) series from lower‐elevation angle GPS observations have higher perturbation amplitudes. Furthermore, the simulated ionospheric disturbance following a vertical Gauss pulse on the ground based on the finite difference time domain method confirms the ionospheric Rayleigh wave signature in the near field and the vertical ground motion dependence theoretically. The vertical ground motion is the dominant source of the ionospheric Rayleigh wave and affects the CID waveform directly.

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)0.001
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 score0.934
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.056
GPT teacher head0.301
Teacher spread0.246 · 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

Citations44
Published2017
Admission routes1
Has abstractyes

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