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Record W2042649162 · doi:10.1190/1.1707057

Analyzing three-component 3D vertical seismic profiling data

2004· article· en· W2042649162 on OpenAlexaffabout
Jitendra S. Gulati, Robert R. Stewart, John M. Parkin

Bibliographic record

VenueGeophysics · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsBaker Hughes (Canada)Encana (Canada)University of Calgary
Fundersnot available
KeywordsVertical seismic profileBoreholeGeologySeismic surveyDeconvolutionSeismologyGeophoneGeotechnical engineeringAlgorithm

Abstract

fetched live from OpenAlex

Abstract A three-component 3D vertical seismic profile (VSP) was acquired over the Blackfoot oil field in Alberta, Canada. The VSP survey was recorded simultaneously with a surface seismic program. The objectives of the VSP were to develop recording logistics, data handling, and processing procedures and to determine if the 3D VSP volumes could image the glauconitic sand reservoir of the Blackfoot field. Dynamite shots from the surface seismic survey, which fell within a 2200-m offset from the recording well, were used in the VSP analysis. The shots were recorded by a string of three-component borehole receivers that was moved seven times, resulting in a receiver depth range of 400 to 910 m. The borehole data were processed using basic VSP processing techniques that included hodogram analysis, wavefield separation using median filters, and VSP deconvolution. The final P-P and P-S image volumes were obtained by VSP common-depth point and VSP common-conversion point stacking the upgoing wavefields followed by f-xy deconvolution. The P-P and P-S images from the VSP correlate well with those from the surface seismic survey. Time slices from the VSP also indicate the trend of the sand channel of the Blackfoot field.

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 score0.816
Threshold uncertainty score0.774

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)0.0000.001

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 head0.029
GPT teacher head0.238
Teacher spread0.209 · 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

Citations15
Published2004
Admission routes2
Has abstractyes

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