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Record W2147253037 · doi:10.1190/1.2213951

Improved Green's functions for passive-source structural studies

2006· article· en· W2147253037 on OpenAlexafffundabout
Jean-Philippe Mercier, M. G. Bostock, Adam Baig

Bibliographic record

VenueGeophysics · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReceiver functionSeismogramGeologyDeconvolutionFunction (biology)LithosphereSeismologyComponent (thermodynamics)Computer scienceAlgorithmPhysics

Abstract

fetched live from OpenAlex

Abstract Over the past two decades, teleseismic receiver functions have proved to be a useful tool to investigate crustal structure. Because they represent a first-order approximation to the S-wave component of the teleseismic-P Green's function, receiver functions provide valuable information on physical properties related to shear modulus. However, the implicit use of the P-component seismogram as a proxy for the source precludes the recovery of information on discontinuous structure involving contrasts in compressional modulus. By deconvolving improved estimates of complex source time functions generated by earthquakes, one may move beyond the conventional receiver function paradigm to a more accurate approximation of the earth's Green's function. Using a new deconvolution method, we present estimates of the P-component of the teleseismic-P Green's functions at several stations of the Canadian National Seismic Network (CNSN) that clearly show the receiver-side pure P-wave crustal multiple. The identification and characterization of these signals in studies of the lithosphere will afford better constraints on subsurface lithology and represent a narrowing of the gap between active- and passive-source seismic imaging.

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 score0.656
Threshold uncertainty score0.829

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 head0.218
Teacher spread0.206 · 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

Citations38
Published2006
Admission routes3
Has abstractyes

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