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Record W2515592211 · doi:10.1002/2016jb013085

Characteristics of slow earthquakes in the very low frequency band: Application to the Cascadia subduction zone

2016· article· en· W2515592211 on OpenAlexaboutno aff
Satoshi Ide

Bibliographic record

VenueJournal of Geophysical Research Solid Earth · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsSeismologySubductionGeologyVery low frequencySeismogramSeismometerEpisodic tremor and slipFrequency bandGeophysicsGeodesyTectonicsBandwidth (computing)Physics

Abstract

fetched live from OpenAlex

Abstract Deep tectonic tremors detected in many subduction zones worldwide are often accompanied by very low frequency (VLF) signals, detectable by broadband seismometers but usually hidden in large ambient noise. By stacking broadband seismograms relative to tremor hypocentral times, we can recover the VLF signals. The stacked signals are then inverted to determine a moment tensor solution, using a procedure previously applied to VLF signals in Japan, Taiwan, and Mexico. Here we apply this method to the Cascadia subduction zone, where tremors and slow slip events are clearly observed. As expected, we successfully recover VLF signals for almost the entire tremor region beneath southern Vancouver Island and northern Washington State. The moment tensors are mostly well determined as low‐angle thrust type, but source depths are poorly constrained. The slip direction is slightly rotated counterclockwise with respect to the local plate motion direction, probably due to bending of the subducting plate. The seismic moment measured in VLF band is proportional to the seismic energy of tremors, with a scaled energy of about 3 × 10 −9 . The widespread observability of VLF signals suggests that the deformation associated with tremors and SSEs is actually a very broadband phenomenon, as suggested by stochastic models.

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)0.002
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 score0.784
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.291
Teacher spread0.266 · 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

Citations47
Published2016
Admission routes1
Has abstractyes

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