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Seismic velocity structure of the easternmost segment of the Gofar transform fault, East Pacific Rise

2022· preprint· en· W4311107789 on OpenAlexaff
Clément Estève, Yajing Liu, J. Gong, Wenyuan Fan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsSeismologyGeologyTransform faultCrustSeismometerInduced seismicitySeismic zoneSlippingFault (geology)GeophysicsGeometry

Abstract

fetched live from OpenAlex

The fast-slipping (~ 14 cm/yr) Gofar transform fault (GTF) East Pacific Rise has three active segments G1 to G3 from east to west. These fault segments produce large earthquakes (MW ~ 6.0) quasi-periodically every five to six years (Figure 1). Interestingly, large earthquakes rupture the same 20-km long fault patches separated by a ~ 10-km rupture barrier zone, implying along-strike variations in fault zone material properties (McGuire et al. 2012). This further suggests that rupture patches and barrier zones remain stable over multiple seismic cycles. Here, we jointly invert P- and S-wave travel times, recorded at an ocean-bottom-seismometer (OBS) experiment along G1 between January 2019 and February 2020, to determine the 3-D seismic velocity structure of the area and relocate the local earthquakes. Our velocity models reveal a large low-velocity anomaly extending through the entire oceanic crust along G1 with some along-strike variations. We identify a 10 km-long rupture barrier zone, which is interpreted to be highly fractured with enhanced fluid circulation. We further suggest that the observed deep seismicity underlying the rupture barrier zone may indicate sea-water infiltration. Lastly, we apply the same approach to the westernmost segment of the GTF and observe some similarities and differences between the two fault zone seismic velocity structures.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.225

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.205
Teacher spread0.190 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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