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Record W2055298247 · doi:10.1190/geo2011-0482.1

Performance of low-fold scalar migration for downhole seismic imaging of massive sulfide ore deposits at Norman West, Sudbury, Canada

2012· article· en· W2055298247 on OpenAlexaffabout
Christof Mueller, Gilles Bellefleur, Erick Adam, Gervais Perron, Marko Mah, D. B. Snyder

Bibliographic record

VenueGeophysics · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsAlberta EnergyMira Geoscience (Canada)Cégep de RimouskiUniversité du Québec à RimouskiGeological Survey of Canada
Fundersnot available
KeywordsGeologyBoreholeSeismologyFold (higher-order function)Vertical seismic profileInduced polarizationGeophysical imagingSeismic migrationMineralogyGeochemistryPetrologyPaleontology

Abstract

fetched live from OpenAlex

ABSTRACT The Downhole Seismic Imaging consortium conducted two consecutive vertical seismic profiling surveys in the Norman West mining camp (Sudbury, Canada) in 1998 and 1999. These were aimed toward imaging a massive sulfide ore deposit situated within the footwall of the Sudbury Igneous Complex (SIC). Three-component seismic data were acquired in four boreholes with variable signal-to-noise ratio and poor polarization quality. Consequently, the images suffered from strong azimuthal ambiguity. A strike filter, passing only reflections originating from within the SIC, was applied during migration to enhance interpretability of the images obtained. Migrated images showed structures correlating with the known position of an ore deposit located 1800 m away from one borehole (N40). Diffraction coherency migration enhanced the image of the deposit, and suggested strong seismic scattering from within the footwall of the SIC.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.876

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.182
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2012
Admission routes2
Has abstractyes

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