MétaCan
Menu
← Back to cohort
Record W2967893177 · doi:10.1190/segam2019-3214763.1

Synthetic modelling to recognize potential duplex waves from basement faults in western Canada

2019· article· en· W2967893177 on OpenAlexaffabout
Eneanwan Ekpo, David W. Eaton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGeologySeismologyBasementReflection (computer programming)Computer scienceEngineering

Abstract

fetched live from OpenAlex

Delineation and mapping of basement faults is important in hydrocarbon exploration and induced seismicity projects. In Alberta, basement faults have exerted influence on the depositional setting of sedimentary rocks of the Western Sedimentary Canadian Basin (WSCB). However, the basement roots for these faults may be vertical or near vertical and therefore could be difficult to image. Duplex-wave migration is an imaging tool used to map such vertical features. This synthetic modelling study aims at identifying different scenarios in which the duplex waves could be observable within existing datasets. Finite difference modelling of acoustic waves was used in creating the velocity model and shot records of the wave field of different scenarios. In addition, two field examples are taken from a deep crustal seismic profile acquired as part of the LITHOPROBE PRAISE program. The field geometry has a pattern suitable for the occurrence of the duplex wave energy. The Winagami Reflection Sequence interpreted as sill intrusions are strong sub horizontal reflections observed on the seismic profile. In order to enhance the stability of the process, a base sub horizontal reflection boundary should be specified. These basement features, if properly mapped can add more insights into the nature of fault reactivation and help in developing a tectonic model which could significantly improve predictive capabilities for induced seismicity risk assessment. Presentation Date: Tuesday, September 17, 2019 Session Start Time: 1:50 PM Presentation Start Time: 4:45 PM Location: 304A Presentation Type: Oral

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.010
GPT teacher head0.183
Teacher spread0.173 · 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
Published2019
Admission routes2
Has abstractyes

Explore more

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