MétaCan
Menu
Back to cohort
Record W1980935820 · doi:10.3997/2214-4609-pdb.177.71

Sv-Wave And P-Wave High Resolution Seismic Reflection Using Vertical Impacting And Vibrating Sources

2008· article· en· W1980935820 on OpenAlexaffabout
A J -M Pugin, S E Pullan, James A. Hunter

Bibliographic record

Venue21st EEGS Symposium on the Application of Geophysics to Engineering and Environmental Problems · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsGeologySeismologyReflection (computer programming)Seismic waveSurface waveHammerShear wavesHigh resolutionVertical seismic profileAcousticsShear (geology)OpticsRemote sensingEngineeringPhysicsStructural engineeringPaleontology

Abstract

fetched live from OpenAlex

Shear wave seismic reflection profiles are generally acquired using sources and receivers horizontally oriented to avoid interferences of the signal with unwanted energies like converted- or P-waves. With various tests using a sledge hammer impulsive source, we have defined a window of acquisition for SV-wave recording. The SV reflected signal can be differentiated from the surface waves in the frequency domain. Our tests have been conducted on the Champlain Sea sediments in the vicinity of Ottawa, Ontario, Canada. These Sediments have already proven to be ideal for P-wave and SH-wave high-resolution seismic imaging. SV sections using a vibrating source otbained over such environment display the highest resolution we have yet observed in any land-based seismic reflection survey. Tests over more compacted sediments have been less conclusive but it is hypothesized that this new SV high resolution seismic reflection method may be used in sedimentary environments that are already kwown to give good results with the SH-wave reflection method. The greatest benefit of the method is that with the same raw shot records it is possible to process a P and an SV high resolution seismic section.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score0.432

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 head0.014
GPT teacher head0.180
Teacher spread0.166 · 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 designSimulation or modeling
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

Citations13
Published2008
Admission routes2
Has abstractyes

Explore more

Same venue21st EEGS Symposium on the Application of Geophysics to Engineering and Environmental ProblemsSame topicSeismic Imaging and Inversion TechniquesFrench-language works237,207