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Record W2129166366 · doi:10.1190/1.2949254

Seismic velocity model building in an area of complex geology, southern Alberta, Canada

2008· article· en· W2129166366 on OpenAlexaffabout
J. Helen Isaac, Don C. Lawton

Bibliographic record

VenueGeophysics · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsGeoscience BCUniversity of Calgary
Fundersnot available
KeywordsPrestackGeologyFlatteningSeismologySeismic velocityOffset (computer science)Regional geologyGeophysical imagingGeodesyTectonicsVolcanism

Abstract

fetched live from OpenAlex

Abstract We developed velocity models to prestack depth migrate two seismic lines acquired in an area of complex mountainous geology in southern Alberta, Canada. Initial processing in the time domain was designed to attenuate noise and enhance the signal in the data. The prestack and poststack time-migrated sections were poorly focused, implying the velocity models would be inadequate for prestack depth migration. The velocity models for prestack depth migration, developed by flattening reflections on common image gathers, ineffectively imaged the complex geology. We developed our most effective velocity models by integrating the mapped surface geology and dips, well formation tops, geological cross sections, and seismic-velocity information into the interpretation of polygonal areas of constant velocity on several iterations of prestack depth-migrated seismic sections. The resulting depth-processed sections show a more geologically realistic geometry for the reflectors at depth and achieve better focusing than either the time-migrated sections or the depth sections migrated with velocity models derived by flattening reflections on offset gathers.

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 score0.738
Threshold uncertainty score0.485

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 head0.027
GPT teacher head0.210
Teacher spread0.182 · 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

Citations3
Published2008
Admission routes2
Has abstractyes

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