MétaCan
Menu
← Back to cohort
Record W2515368847 · doi:10.1190/segam2016-13856947.1

Facies-dependent AVO prediction: A framework for prospect derisking in the Flemish pass and orphan basins

2016· article· en· W2515368847 on OpenAlexaffabout
Nick Huntbatch, A. Selnes, Neil Whitfield, Ian Atkinson, Richard Wright, D. Cameron, D. McCallum

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsNalcor Energy (Canada)
Fundersnot available
KeywordsFlemishFaciesGeologyGeomorphologyStructural basinGeographyArchaeology

Abstract

fetched live from OpenAlex

A predictive framework has been defined that relates geological processes to seismic AVO response away from well control, in the Flemish Pass and Orphan Basins and offshore Labrador. The framework provides a link between geological properties and processes, and the elastic response of the rocks encountered in these basins. A balanced approach is taken using both empirical trends and analytical rock physics models to ensure that the behavior of each facies is captured in the most appropriate manner. The facies dependent predictions made by the framework are used to develop synthetic AVO models for different scenarios, which can then be compared to seismic AVO anomalies identified within the basins. For illustrative purposes, the framework is deployed to investigate a seismic amplitude anomaly within a rotated fault block. Presentation Date: Tuesday, October 18, 2016 Start Time: 8:50:00 AM Location: Lobby D/C Presentation Type: POSTER

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.017
GPT teacher head0.231
Teacher spread0.214 · 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 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

Citations0
Published2016
Admission routes2
Has abstractyes

Explore more

Same topicSeismic Imaging and Inversion Techniques→French-language works237,207→