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Record W4404484335 · doi:10.1080/07038992.2024.2424768

RADARSAT-2 DInSAR and GNSS-Derived Finite Fault Model of the 2012 Mw 7.8 Haida Gwaii Earthquake

2024· article· en· W4404484335 on OpenAlexaffvenue
Sergey Samsonov, Yan Jiang

Bibliographic record

VenueCanadian Journal of Remote Sensing · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsUniversity of VictoriaGeological Survey of CanadaNatural Resources Canada
Fundersnot available
KeywordsGNSS applicationsSeismologyGeographyGeoreferenceFault (geology)GeologyRemote sensingGlobal Positioning SystemComputer sciencePhysical geographyTelecommunications

Abstract

fetched live from OpenAlex

The Mw 7.8 Haida Gwaii earthquake occurred on 28 October 2012, generating up to 13 m tsunami waves and 3 m run-up along the British Columbia coastline. Despite the magnitude of the earthquake and tsunami, damages were minor due to the lack of vulnerable infrastructure in the remote area. Previous finite fault models were derived from GNSS, seismic and tsunami data, but the uncertainty remained high due to the limited number of seismic and GNSS stations near the epicenter. In this study, finite fault models were developed using RADARSAT-2 interferograms and previously published GNSS data. These models defined the location of the fault and provided a detailed slip distribution with a high degree of certainty. The results confirmed that the main slip was located on the subduction fault interface between the Pacific and North American plates, west of the Queen Charlotte Fault. The estimated moment magnitude of 7.88–7.93 is slightly larger than the previously reported moment magnitude of around 7.8, due to the capture of postseismic deformation in the interferograms. Overall, the study provides an improved finite fault slip model for the Haida Gwaii earthquake and highlights the importance of utilizing remote sensing data for studying earthquakes in remote 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 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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.991
Threshold uncertainty score0.988

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 head0.024
GPT teacher head0.207
Teacher spread0.184 · 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 designOther design
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

Citations3
Published2024
Admission routes2
Has abstractyes

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