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Record W2094076112 · doi:10.1029/2003jb002783

Green's functions, source signatures, and the normalization of teleseismic wave fields

2004· article· en· W2094076112 on OpenAlexaffabout
M. G. Bostock

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSeismogramReceiver functionSeismometerDeconvolutionScatteringNormalization (sociology)GeologyMantle (geology)Source functionFunction (biology)SeismologyPhysicsGeophysicsOpticsAstrophysics

Abstract

fetched live from OpenAlex

We examine the canonical source/Green's function separation problem in the context of teleseismic P wave scattering from receiver‐side crust and upper mantle structure. Conventional “receiver function” analysis affords a leading order approximation to the S component of the Green's function but provides no information on P ‐to‐ P scattering. We demonstrate that an improved estimate of the three‐dimensional Earth's Green's function, including scattered P contributions, can be achieved through consideration of its theoretical spectral properties. Under conditions typical of the real Earth the P component of the Green's function is shown to be minimum phase. This behavior is responsible for the success of receiver functions in mantle studies. The minimum‐phase property is used here to normalize the source signature on P wave seismograms, thereby facilitating implementation of multichannel, multicomponent deconvolution of both Green's function and source signature within the log spectral domain. Examples using both synthetic simulations and seismograms recorded on the Canadian National Seismograph Network illustrate the recovery of accurate and reproducible estimates of the P wave Green's function. Our approach can be adapted to a range of source‐receiver configurations. In particular, it may prove useful in the recovery of compressional properties beneath portable, field arrays where calibration is provided by nearby, permanent installations.

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)0.001
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 score0.901
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.254
Teacher spread0.236 · 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

Citations51
Published2004
Admission routes2
Has abstractyes

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