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Record W2481212130 · doi:10.1093/gji/ggw217

3-D<i>P</i>- and<i>S</i>-wave velocity structure and low-frequency earthquake locations in the Parkfield, California region

2016· article· en· W2481212130 on OpenAlexaff
Xiangfang Zeng, C. H. Thurber, D. R. Shelly, R. M. Harrington, E. S. Cochran, N. L. Bennington, D. E. Peterson, Bin Guo, K. McClement

Bibliographic record

VenueGeophysical Journal International · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsMcGill University
FundersSouthern California Earthquake CenterNational Science Foundation
KeywordsSeismologyGeologyMagnetotelluricsSan andreas faultCrustFault (geology)Data setGeophysicsElectrical resistivity and conductivityElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

To refine the 3-D seismic velocity model in the greater Parkfield, California region, a new data set including regular earthquakes, shots, quarry blasts and low-frequency earthquakes (LFEs) was assembled. Hundreds of traces of each LFE family at two temporary arrays were stacked with time–frequency domain phase weighted stacking method to improve signal-to-noise ratio. We extend our model resolution to lower crustal depth with LFE data. Our result images not only previously identified features but also low velocity zones (LVZs) in the area around the LFEs and the lower crust beneath the southern Rinconada Fault. The former LVZ is consistent with high fluid pressure that can account for several aspects of LFE behaviour. The latter LVZ is consistent with a high conductivity zone in magnetotelluric studies. A new Vs model was developed with S picks that were obtained with a new autopicker. At shallow depth, the low Vs areas underlie the strongest shaking areas in the 2004 Parkfield earthquake. We relocate LFE families and analyse the location uncertainties with the NonLinLoc and tomoDD codes. The two methods yield similar results.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.413

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 head0.011
GPT teacher head0.204
Teacher spread0.192 · 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

Citations27
Published2016
Admission routes1
Has abstractyes

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